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This paper is a novel study on crack detection of industrial explosives. The proposed algorithm consists of the following steps:(1) image preprocessing was performed according to the defect features of industrial explosives cartridge, and we developed an improved visual attention based algorithm. This proposed algorithm features a parametric analysis that can be implemented on the image according to the conspicuous maps with the introduction of the concept of defect discrimination ; (2) as compared with other algorithms, our method can realize real-time multitarget detection function; (3) a new analysis method, the IPV-WEN algorithm, was proposed to analyze the cartridge defects based on performance indices. Through comparison and experimentation, it was revealed that this method can achieve a detection accuracy of 97.9%, with detection time of 34.51 ms, which satisfied the requirement in the industrial explosives production.

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This paper presents a geometrical-information-assisted approach for matching local features. With the aid of Bayes’ theorem, it is found that the posterior confidence of matched features can be improved by introducing global geometrical information given by distances between feature points. Based on this result, we work out an approach to obtain the geometrical information and apply it to assist matching features. The pivotal techniques in this paper include (1) exploiting elliptic parameters of feature descriptors to estimate transformations that map feature points in images to points in an assumed plane; (2) projecting feature points to the assumed plane and finding a reliable referential point in it; (3) computing differences of the distances between the projected points and the referential point. Our new approach employs these differences to assist matching features, reaching better performance than the nearest neighbor-based approach in precision versus the number of matched features.

In order to solve the problem that the surface electromyography signal is not accurate enough for the quantitative identification of the human motion angle, this paper used PCA-based third-order cumulant analysis method to extract sEMG signal features, and used this features as input for a single-layer BP neutal network to predict the joint angle of the human body. In the identification of shoulder joint motion, the root mean square error of the prediction result was 5.19, and the correlation coefficient was 0.95, which is obviously superior to the AR model method and the RMS method.