Super-Resolution-Aided Small-Target Detection Based on Multi-Task Learning
Small targets often exhibit low resolution and blurriness and are easily affected by occlusions and background interference,making accurate and real-time detection of small targets challenging.In this study,to enhance the detection performance,a super-resolution-aided small-target detection algorithm based on multi-task learning called Multi-YOLO is proposed.First,a super-resolution auxiliary branch is introduced to guide the main network in extracting effective features,thereby reducing the loss of information for small targets.Second,a collaborative supervision method is employed by combining Anchor based and Anchor free detection heads to improve the detection accuracy.Additionally,a CTR3 module is used at the end of the backbone network to strengthen the correlation between the target information and position awareness.Finally,during the inference stage,only the detection branch is used to maintain the speed of inference.Experimental results show that,compared with the baseline network,Multi-YOLO achieves performance improvement on the VEDAI,COCO MiniTrain,and SPCD datasets.Specifically,on the VEDAI dataset,this method achieves a 10.9％improvement in mean Average Precision(mAP)improvement while maintaining a model size similar to that of the baseline model.Moreover,compared with mainstream single-stage object detection networks,Multi-YOLO excels in small-target detection,maintaining a remarkable balance between accuracy and speed.
deep learningsmall-target detectionmulti-task learningsuper resolutionattention mechanism
万方数据
维普
