Abstract
An MDCFVit-YOLO model based on the YOLOv8 algorithm is proposed to address issues in nighttime infrared object detection such as low visibility, high interference, and low precision in detecting small objects. The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. The CSM module dynamically adjusts the weights of feature maps, enhancing the model of sensitivity to small targets. The dynamic automated detection head DAIH improves the accuracy of infrared target detection by dynamically adjusting regression feature maps. Additionally, three innovative loss functions-focalerDIoU, focalerGIOU and focalerShapeIoU are proposed to reduce losses during the training process. Experimental results show that the detection accuracy of 78% for small infrared nighttime targets, with a recall rate of 58.6%, an mAP value of 67%. and a parameter count of 20.9M for the MDCFVit-YOLO model. Compared to the baseline model YOLOv8, the mAP increased by 6.4%, with accuracy and recall rates improved by 4.5% and 5.7%, respectively. This research provides new ideas and methods for infrared target detection, enhancing the detection accuracy and real-time performance.