Infrared Target Detection Based on Deep Learning

With the development of artificial intelligence technology, computer vision has gradually entered people's daily lives. In recent years, target detection as an important branch of computer vision has attracted the attention of many scholars and has made some progress. In various application scenarios with target detection and recognition, such as night, haze and other severe weather conditions, the demand and application of target detection for infrared images are becoming more and more extensive. There are obvious differences between infrared images and visible light images. Infrared images have low imaging contrast, unobvious ure features, and more noise. These physical characteristics make infrared target detection always challenging. This paper is based on the one-stage target detection algorithm YOLOv3. In order to improve the detection ability of small targets, we improve the network structure and expand the network to 4 feature scales; by introducing GIOU, the loss function is improved, and the accuracy of network detection is improved; by merging the Batch Normalization layer and the convolutional layer, the speed of network inference is speeded up. Experimental results show that compared with the original YOLOv3 network, the improved YOLOv3-4GB network has improved detection accuracy and enhanced detection capabilities for small targets; this paper deploys the improved algorithm on the embedded platform to meet the requirements of real-time detection.