Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Infrared small target detection is still a challenge in the field of object detection. At present, although there are many related research achievements, it surely needs further improvement. This paper introduced a new application of severely occluded vehicle detection in the complex wild background of weak infrared camera aerial images, in which more than 50% area of the vehicles are occluded. We used YOLOv4 as the detection model. By applying secondary transfer learning from visible dataset to infrared dataset, the model could gain a good average precision (AP). Firstly, we trained the model in the UCAS_AOD visible dataset, then, we transferred it to the VIVID visible dataset, finally we transferred the model to the VIVID infrared dataset for a second training. Meanwhile, added the hard negative example mining block to the YOLOv4 model, which could depress the disturbance of complex background thus further decrease the false detecting rate. Through experiments the average precision improved from90.34% to 91.92%, the F1 score improved from 87.5% to 87.98%, which demonstrated that the proposed algorithm generated satisfactory and competitive vehicle detection results.