Ship detection in infrared images based on YOLOV4 and salient regions

Infrared image ship detection has important applications in military and civil affairs. Because infrared images are not easy to acquire in large quantities, deep neural networks cannot directly use infrared images for training; if the pre-trained model of visible light images is directly used for detection, the phenomenon of missed detection will be caused due to different imaging conditions. In response to this problem, this paper proposes a detection method that combines a deep convolutional neural network and salient region. Firstly, we proposed a method extracting salient region based on anchor and saliency map, then multiple new images are formed by salient regions, and the newly formed images and the original image are input to the deep convolutional neural network for parallel processing, and finally the results of the detection are integrated to produce the final detection results by the non-maximum suppression (NMS) method. The comparison results show that the method proposed in this paper can effectively reduce the rate of missed detection and thus improve the accuracy of detection