Flood scenarios vehicle detection algorithm based on improved YOLOv9 Flood scenarios vehicle detection algorithm based on improved YOLOv9

• Published in: Multimedia systems Vol. 31; no. 2; p. 74
• Main Authors: Sun, Jiwu, Xu, Cheng, Zhang, Cheng, Zheng, Yujia, Wang, Pengfei, Liu, Hongzhe
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Computer Communication Networks; Computer Graphics; Computer Science; Cryptology; Data Storage Representation; Datasets; Deep learning; Flooding; Floods; Multimedia Information Systems; Object recognition; Obstructions; Operating Systems; Rain; Regular Paper; Rescue operations; Sensors; Storm damage; Traffic; Vehicles; YOLOv9; Flood; Vehicle detection; Dataset
• ISSN: 0942-4962; 1432-1882
• DOI: 10.1007/s00530-024-01661-w

In light of challenges such as vehicle obstructions and road inundation during floods, ascertaining vehicle locations becomes arduous. Existing object detection algorithms are unable to effectively detect vehicles in flood scenarios, which brings great difficulties to the implementation of rescue operations. To address these issues, this study creates a dataset for vehicle detection in flooding scenarios and proposes an improved vehicle detection algorithm, SDF-YOLO, based on YOLOv9. The algorithm uses a multi-size convolutional kernel network SKN(Selective Kernel Networks) to flexibly extract target features with different granularities, which helps to improve the recognition and localisation accuracy of occluded objects. The efficient convolutional algorithm DSConv(Distribution Shifting Convolution) is used to reduce the memory consumption during the training process as well as to improve the convergence speed of the model to quickly extract the key features of the vehicle. In addition, the IoU is replaced with FIIoU, which effectively improves the detection accuracy of difficult-to-classify samples by introducing auxiliary bounding box and scale adjustment strategies. Experimental results demonstrate that the enhanced model, as opposed to the YOLOv9 algorithm, achieved a notable increase in accuracy by 4.1%, F1-score by 1.7%, and mAP by 3.1%. These findings not only contribute to enhancing the efficacy of flood response efforts and mitigating associated human and property losses but also serve to advance the evolution of deep learning-based object detection methodologies within intricate environmental settings.