VODACBD: Vehicle Object Detection Based on Adaptive Convolution and Bifurcation Decoupling

Vehicle object detection is the foundation of autonomous driving system development. The existing state-of-the-art methods mainly focus on the applications and improvement of general-purpose single shot multibox detector (SSD) and you only look once (YOLO) methods. However, these methods overlook the specific characteristics of traffic scenarios, such as frequent changes of camera angles and rapid changes in surrounding environment, thus leading to peculiar deformations and blurring of vehicle objects. To address these issues, we consider making improvements on the targeted deformations, classification, positioning, and other operations for vehicle object images to alleviate the object deformation and blurring, and therefore propose a Vehicle Object Detection method based on adaptive convolution and bifurcation decoupling (VODACBD). Specifically, in VODACBD, to solve the deformation problem of vehicle objects, adaptive convolution, and feature redivision upsampling are proposed to dynamically capture object features; to alleviate the blurring of vehicle objects, a bifurcation decoupling head is proposed to learn vehicle categories, positions, and confidences. Moreover, to further enhance the overall performance, a global optimal transportation algorithm (GlobalOTA) is well designed to improve the quality of training samples. Extensive experiments were conducted on publicly available traffic object detection datasets such as BDD100K, KITTI, and VOC. The experimental results demonstrate that, compared with current state-of-the-art methods, VODACBD not only achieves an average performance improvement of 1.4% but also an average speed improvement of 1.57 times that of the state-of-the-art.