Search results

1 – 10 of over 7000
Article
Publication date: 18 January 2013

Chen Guodong, Zeyang Xia, Rongchuan Sun, Zhenhua Wang and Lining Sun

Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to…

Abstract

Purpose

Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features.

Design/methodology/approach

The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales.

Findings

The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework.

Originality/value

The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.

Article
Publication date: 4 September 2017

Stephan Mühlbacher-Karrer, Juliana Padilha Leitzke, Lisa-Marie Faller and Hubert Zangl

This paper aims to investigate the usability of the non-iterative monotonicity approach for electrical capacitance tomography (ECT)-based object detection. This is of…

Abstract

Purpose

This paper aims to investigate the usability of the non-iterative monotonicity approach for electrical capacitance tomography (ECT)-based object detection. This is of particular importance with respect to object detection in robotic applications.

Design/methodology/approach

With respect to the detection problem, the authors propose a precomputed threshold value for the exclusion test to speed up the algorithm. Furthermore, they show that the use of an inhomogeneous split-up strategy of the region of interest (ROI) improves the performance of the object detection.

Findings

The proposed split-up strategy enables to use the monotonicity approach for robotic applications, where the spatial placement of the electrodes is constrained to a planar geometry. Additionally, owing to the improvements in the exclusion tests, the selection of subregions in the ROI allows for avoiding self-detection. Furthermore, the computational costs of the algorithm are reduced owing to the use of a predefined threshold, while the detection capabilities are not significantly influenced.

Originality/value

The presented simulation results show that the adapted split-up strategies for the ROI improve significantly the detection performance in comparison to the traditional ROI split-up strategy. Thus, the monotonicity approach becomes applicable for ECT-based object detection for applications, where only a reduced number of electrodes with constrained spatial placement can be used, such as in robotics.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 36 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 21 May 2021

Chang Liu, Samad M.E. Sepasgozar, Sara Shirowzhan and Gelareh Mohammadi

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the…

Abstract

Purpose

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

Design/methodology/approach

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

Findings

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

Research limitations/implications

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

Originality/value

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

Open Access
Article
Publication date: 25 March 2021

Bartłomiej Kulecki, Kamil Młodzikowski, Rafał Staszak and Dominik Belter

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the…

Abstract

Purpose

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

Design/methodology/approach

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

Findings

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

Research limitations/implications

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

Practical implications

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

Social implications

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

Originality/value

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

Open Access
Article
Publication date: 1 October 2018

Xunjia Zheng, Bin Huang, Daiheng Ni and Qing Xu

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

2162

Abstract

Purpose

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

Design/methodology/approach

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

Findings

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

Originality/value

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 2
Type: Research Article
ISSN: 2399-9802

Keywords

Open Access
Article
Publication date: 29 July 2020

T. Mahalingam and M. Subramoniam

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which…

Abstract

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

Details

Applied Computing and Informatics, vol. 17 no. 1
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 14 November 2016

Anan Banharnsakun and Supannee Tanathong

Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring…

Abstract

Purpose

Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring system, vehicle detection is an essential and challenging task. In the previous studies, many vehicle detection methods have been presented. These proposed approaches mostly used either motion information or characteristic information to detect vehicles. Although these methods are effective in detecting vehicles, their detection accuracy still needs to be improved. Moreover, the headlights and windshields, which are used as the vehicle features for detection in these methods, are easily obscured in some traffic conditions. The paper aims to discuss these issues.

Design/methodology/approach

First, each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model. Next, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the moving objects frame by frame.

Findings

Using the proposed method, it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement (waving trees), which has to be deemed as background. In addition, the proposed method is able to deal with different vehicle shapes such as cars, vans, and motorcycles.

Originality/value

This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 3 February 2020

Hui Zhang, Jinwen Tan, Chenyang Zhao, Zhicong Liang, Li Liu, Hang Zhong and Shaosheng Fan

This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved…

Abstract

Purpose

This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf.

Design/methodology/approach

To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method.

Findings

The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully.

Originality/value

The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.

Details

Industrial Robot: the international journal of robotics research and application, vol. 47 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 28 June 2022

Akhil Kumar

This work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify…

17

Abstract

Purpose

This work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify persons wearing face masks. In surveillance environments, complete visibility of the face area is a guideline, and criminals and law offenders commit crimes by hiding their faces behind a face mask. The face mask detector model proposed in this work can be used as a tool and integrated with surveillance cameras in autonomous surveillance environments to identify and catch law offenders and criminals.

Design/methodology/approach

The proposed face mask detector is developed by integrating the residual network (ResNet)34 feature extractor on top of three You Only Look Once (YOLO) detection layers along with the usage of the spatial pyramid pooling (SPP) layer to extract a rich and dense feature map. Furthermore, at the training time, data augmentation operations such as Mosaic and MixUp have been applied to the feature extraction network so that it can get trained with images of varying complexities. The proposed detector is trained and tested over a custom face mask detection dataset consisting of 52,635 images. For validation, comparisons have been provided with the performance of YOLO v1, v2, tiny YOLO v1, v2, v3 and v4 and other benchmark work present in the literature by evaluating performance metrics such as precision, recall, F1 score, mean average precision (mAP) for the overall dataset and average precision (AP) for each class of the dataset.

Findings

The proposed face mask detector achieved 4.75–9.75 per cent higher detection accuracy in terms of mAP, 5–31 per cent higher AP for detection of faces with masks and, specifically, 2–30 per cent higher AP for detection of face masks on the face region as compared to the tested baseline variants of YOLO. Furthermore, the usage of the ResNet34 feature extractor and SPP layer in the proposed detection model reduced the training time and the detection time. The proposed face mask detection model can perform detection over an image in 0.45 s, which is 0.2–0.15 s lesser than that for other tested YOLO variants, thus making the proposed detection model perform detections at a higher speed.

Research limitations/implications

The proposed face mask detector model can be utilized as a tool to detect persons with face masks who are a potential threat to the automatic surveillance environments such as ATMs, banks, airport security checks, etc. The other research implication of the proposed work is that it can be trained and tested for other object detection problems such as cancer detection in images, fish species detection, vehicle detection, etc.

Practical implications

The proposed face mask detector can be integrated with automatic surveillance systems and used as a tool to detect persons with face masks who are potential threats to ATMs, banks, etc. and in the present times of COVID-19 to detect if the people are following a COVID-appropriate behavior of wearing a face mask or not in the public areas.

Originality/value

The novelty of this work lies in the usage of the ResNet34 feature extractor with YOLO detection layers, which makes the proposed model a compact and powerful convolutional neural-network-based face mask detector model. Furthermore, the SPP layer has been applied to the ResNet34 feature extractor to make it able to extract a rich and dense feature map. The other novelty of the present work is the implementation of Mosaic and MixUp data augmentation in the training network that provided the feature extractor with 3× images of varying complexities and orientations and further aided in achieving higher detection accuracy. The proposed model is novel in terms of extracting rich features, performing augmentation at the training time and achieving high detection accuracy while maintaining the detection speed.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 21 June 2021

Zhoufeng Liu, Shanliang Liu, Chunlei Li and Bicao Li

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep…

Abstract

Purpose

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.

Design/methodology/approach

To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.

Findings

The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).

Research limitations/implications

Our proposed algorithm can provide a promising tool for fabric defect detection.

Practical implications

The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.

Social implications

This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial

Originality/value

Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Details

International Journal of Clothing Science and Technology, vol. 34 no. 2
Type: Research Article
ISSN: 0955-6222

Keywords

1 – 10 of over 7000