Typical Target Detection for Infrared Homing Guidance Based on YOLO v3

The traditional infrared target detection method for missile homing guidance is flawed because of low accuracy and lack of real-time feedback. Therefore, an infrared homing guidance target detection method based on the improved YOLO v3 is proposed,and it involves the optimization of the weight loss by considering the background of infrared homing guidance, improving the accuracy of positioning and classification. Subsequently, the adaptive moment estimation (Adam) and stable stochastic gradient descent (SGD) with momentum are fully exploited. Further, a joint training method predicated on pre-training, which significantly improves the accuracy of detection, is proposed herein. The improved algorithm is ideally trained and tested on the infrared target dataset designed in this work. The best mean average precision is 77.89%, and all the detection rates are greater than 25 frame/s. The false and missing alarm probabilities are effectively reduced.