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The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.