| Abstract |
Urban drainage pipe system is an important part of city management. Automated detection of the status of storm drain in street-level images through current technologies in computer vision and AI is an important aspect of smart city construction. In this paper, a framework based on YOLOv5s for storm drain detection (YOLO-SDD) in street view is proposed. By analyzing the characteristics of small-scale targets, YOLO-SDD focuses on optimizing the Backbone network and its loss function. Series of experiments demonstrated that in the task of detecting different states of storm drain under various environmental conditions, the mean average precision (mAP@.5) of the YOLO-SDD can reach 89.6%, increasing by 2% compared with the baseline model YOLOv5s. In the presence and absence of occlusion, the average precision of storm drain detection increased by 0.9% and 3.1%, respectively. In addition, the effectiveness and generalization ability of YOLO-SDD were further validated using the storm drain dataset of Urbana-Champaign (SDUC) from Illinois, USA, and the dataset for object detection in aerial images (DOTA). Finally, this work has deployed the YOLO-SDD on the Android system, which verifies its ability of real-time detecting storm drain in different states in street scenes. © Shanghai Jiao Tong University 2024. |
