Lightweight Semantic Feature Extraction Model With Direction Awareness for Aerial Traffic Object Detection
Jiaquan Shen, Ningzhong Liu, Han Sun, Shang Wu, Zongzheng Liang, Lulu Han, Yongxin Zhang, Deguang Li
Abstract
The detection of traffic objects in aerial scenes holds significant application potential in both military and civilian sectors. However, current aerial traffic object detection techniques based on computer vision face challenges including limited awareness of object direction, a heavy computational burden on the feature extraction backbone network, and inadequate capacity to learn crucial semantic information. In this paper, our focus is on investigating the mechanisms for predicting the directional perception of traffic objects in aerial scenes, achieving backbone network lightness, and exploring methods for extracting key semantic information from objects. Firstly, to tackle the challenge of poor perception of traffic object direction and angle in aerial scenes, we utilize techniques like equivariant vector field convolution, multi-task anchor-free prediction, and adaptive loss to develop a precise mechanism for recognizing and predicting object directions. Secondly, given the presence of small-sized and numerous objects in aerial scenes, we propose the adoption of a lightweight backbone network employing channel stacking to decrease the model’s computational burden. Additionally, we establish a theoretical framework and methodology for optimizing and compressing this backbone network, aimed at enhancing feature extraction and propagation for aerial traffic objects. Furthermore, to address the issue of inadequate learning of key semantic information features, we incorporate saliency attention and multi-scale contextual information to capture the essential semantic characteristics of the objects. We also establish a method for extracting semantic features specifically for aerial traffic objects. The approach presented in this paper broadens the applicability of aerial object detection algorithms and offers novel methodologies and theoretical foundations for object detection in intricate scenarios.