Research on multivariate abnormal image detection in coal mine transportation system

There are various types and scenarios of abnormal risks in coal mine transportation systems. The occurrence of abnormal accidents at coal mine sites is accidental, and the number of abnormal samples obtained is much smaller than that of normal samples, resulting in an imbalance of positive and negative samples. In order to solve the above problems, a multivariate abnormal image detection method for coal mine transportation systems based on hypersphere reconstructed data description (HRDD) is proposed. On the basis of full convolutional data description (FCDD), an image reconstruction auxiliary task is introduced. The mean square error loss function is selected as the objective function of the image reconstruction auxiliary task. Abnormal image detection and positioning are quantified as an inequality constrained optimization problem. The seamless fusion technology is used to fuse auxiliary datasets and abnormal samples into normal samples, in order to reduce the difference between abnormal fusion samples and normal samples, expand the total number of abnormal samples, and balance the proportion of abnormal and normal samples. Through multiple sets of noise simulation experiments and on-site experiments, it has been proven that adding Gaussian noise to the resistance zone with a certain probability for enhanced training can improve the noise resistance efficiency, generalization capability, detection accuracy, and other aspects of the HRDD model. The results of the ablation experiment show that the auxiliary dataset effectively improves the problem of sample imbalance, with an accuracy increase of 36.5%. The introduction of image reconstruction auxiliary tasks can ensure that deep features can be accurately mapped to abnormal positions, resulting in an IoU improvement of 33.4%. There is a strong coupling effect between the auxiliary dataset and the image reconstruction auxiliary task. The combination of the two can further stimulate the performance potential of the HRDD algorithm. The addition of seamless fusion samples and Gaussian noise enhancement has to some extent improved the generalization capability of the HRDD model. The comparative experimental results show that the accuracy and IoU of the HRDD algorithm are better than other mainstream algorithms. Compared with the FCDD algorithm, the accuracy and IoU of the HRDD algorithm have increased by 4.6% and 7.0% respectively, making it more suitable for coal mine sites.