Detection of River Floating Debris in UAV Images Based on Improved YOLOv5

Rivers are an essential part of the aquatic environment. The accurate detection and timely cleaning of river floating debris plays a vital role in the landscape and aquatic ecological environment. At present, the detection of river floating debris mainly relies on manually patrolling rivers and fixed camera monitoring, which has the problem of low efficiency and high cost. In this paper, we used Unmanned Aerial Vehicle (UAV) to photograph rivers and then utilized deep learning algorithms to detect floating debris in the images to improve the efficiency of river regulation. However, its application faces some problems, including the lack of datasets, the complex background of UAV images, and the small and sparsely distributed objects. To address the above issues, we used a UAV to capture river images and labeled the floating debris to construct a dataset. Moreover, to enhance the detection capability of river floating debris, we proposed an improved algorithm based on YOLOv5s. Firstly, the algorithm adds a microscale detection layer in the detection phase to improve small targets' detection. Secondly, it introduces an improved CBAM in the feature fusion phase to suppress the effects of useless and complex background information. Finally, in the loss function, a weighting factor is added to objectness loss to raise the loss weight of positive samples to ameliorate where negative samples loss much more than positive samples. The experimental results illustrated that compared to the baseline, our method has superior precision, recall, and mAP@0.5, reaching 87.4%, 85.6%, and 91.8%, respectively. The proposed method can accurately detect river floating debris in UAV images and provide technical support for river regulation.