An advanced YOLOv3 method for small-scale road object detection

• Published in: Applied soft computing 2021-11, Vol.112, p.107846, Article 107846
• Main Authors: Wang, Kun, Liu, Maozhen, Ye, Zhaojun
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Deep learning; Road object detection; YOLOv3
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107846

Road target detection is a very challenging task in the field of computer vision because it is easily affected by complex backgrounds and sparse features of small targets. YOLOv3 (You Only Look Once v3) is currently one of the state-of-the-art object detection methods of deep learning. However, because the k-means clustering algorithm is sensitive to the initial clustering center, the local fragile visual field features related to small objects in the prediction map are severely lost and the final decision-making theory (The grid located in the center of the foreground object is responsible for predicting this object) of the network ignores the detailed information of the neighboring grid, there are still many problems in object detection. In this paper, we propose an improved algorithm based on YOLOv3 for small-scale object detection. We use the improved k-medians clustering method instead of the previous k-means to improve the model instability caused by the singularity; We propose a local enhancement method to strengthen weak features for small-scale object detection by paralleling a branch on the backbone. Besides, a flexible offset sampling structure added in parallel for information compensation is also designed. A series of experiments showing that our system has achieved good detection results on the KITTI and UA-DETRAC public datasets, and the distinguishing performance for small-scale objects is significantly improved. Therefore, our method is effective in road target detection tasks. •The paper focuses on the long-distance detection problem with rare features.•The clustering algorithm effectively reduces the sensitivity to shields noise.•A local feature enhancement algorithm that focuses on fragile vision is proposed.•The strategy of offset sampling combines more comprehensive target features.•The aid of weights for final decision-making, especially for small targets.