Infrared Armored Target Detection Based on Edge-perception in Deep Neural Network

Automatic detection of armored targets has always been the most challenging problem in the field of infrared guidance. Traditional models address this problem by extracting the low-level features of an object and then training the feature classifier. However, because traditional detection algorithms can not cover all object patterns, the detection performance in practical applications is limited. Inspired by the edge-aware model, this study proposes an improved deep network based on edge perception. The network improves the accuracy of the armored contour through an edge-aware fusion module. By exploiting he advantages of the feature extraction module and context aggregation module, it can better adapt to the shape changes of objects and has high detection and recognition accuracy. The results show that the proposed armored detection network model can effectively improve the accuracy of detection and positioning in infrared images.