Research on UAV Small Object Detection Method Improved by YOLOv5

doi:10.3778/j.issn.1002-8331.2309-0454

Abstract

Abstract: Small objects in images captured by UAV have the characteristics of unclear feature information, complex backgrounds, and partial target occlusion, which leads to problems of false detection and missed detection during detection. In response to these problems, an improved UAV small target detection method SDT-YOLOv5 is proposed. Firstly, the dataset images are sliced to increase the proportion of small objects in each slice and improve the recognition ability of small objects. Secondly, a dynamic decoupled detection head is used to introduce dynamic convolution and adaptive receptive field mechanism while decoupling the classification and regression branches of the detection head to achieve stronger feature expression and extraction capabilities. Finally, an optimal transmission allocation method based on intersection-union ratio loss of minimum point distance is proposed. The distance between the upper left corner and lower right corner of the predicted bounding box and the real bounding box is minimized, and then based on the position information and distance measurement of the bounding box, with the goal of minimizing the total cost, the optimal real bounding box and predicted bounding box matching scheme is found to improve detection accuracy. Through experimental results on the VisDrone2019 dataset, the mAP50 value of the improved YOLOv5 reaches 58.5%, which is 23.2 percentage points higher than the mAP50 value of the original YOLOv5. This shows that the improved method effectively improves the detection accuracy of UAV small objects and can detect small objects more accurately.

Key words: YOLOv5, small object detection, detection head, loss function

摘要： 无人机拍摄图像的小目标具有特征信息不明显、背景复杂和部分目标遮挡的特点，导致检测时会出现误检和漏检的问题。针对这些问题，提出一种改进YOLOv5的无人机小目标检测方法SDT-YOLOv5。首先对数据集图像进行切片处理，使得每个切片中小目标的占比变高，提高小目标的识别能力。其次采用动态解耦检测头，引入动态卷积和自适应感受野机制，同时将检测头的分类和回归分支解耦，以实现更强的特征表达和提取能力。最后提出了基于最小点距离的交并比损失的最佳传输分配方法。最小化预测边界框与真实边界框之间的左上角和右下角点距离，再基于边界框的位置信息和距离度量，以最小化总成本为目标，找到最优的真实边界框与预测边界框匹配方案，提高检测的准确性。在VisDrone2019数据集上进行实验，结果显示改进YOLOv5的mAP50值达到了58.5%，相比于原始YOLOv5的mAP50值提高了23.2个百分点。这表明改进方法有效地提高了无人机小目标的检测精度，能够更准确地检测到小目标。

关键词: YOLOv5, 小目标检测, 检测头, 损失函数

BAI Yu, ZHOU Yanyuan, AN Shengbiao. Research on UAV Small Object Detection Method Improved by YOLOv5[J]. Computer Engineering and Applications, 2024, 60(10): 276-284.

白宇, 周艳媛, 安胜彪. 改进YOLOv5的无人机小目标检测方法研究[J]. 计算机工程与应用, 2024, 60(10): 276-284.

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