Abstract
Convolutional neural networks are advanced computer vision solution for various tasks, and have made considerable progress. In order to detect infrared targets with details and texture features, we propose a detection method based on YOLOv3. First, we propose a bidirectional feature fusion structure called Improve-FPN(Im-FPN), which allows fast and efficient multi-scale feature fusion. Second, we use the Focal Loss(FL) loss function to balance the problem of sample imbalance. Based on these optimizations, an Infrared-YOLO (IYOLO) target detection model for infrared target is constructed. On the self-constructed infrared dataset: Infrared-VOC, the mAP of IYOLO can achieve 77.1%, which is 4.0% higher than YOLOv3, and the detection speed can reach 55.6FPS. Experimental results show that this method can improve the overall accuracy of detection while ensuring real-time detection.
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