Abstract
Abstract
Detecting vehicles from a surveillance viewpoint is essential, as it has wide applications in community security and traffic control. However, existing methods completely overlook the high memory access costs (MAC) and low degree of parallelism inherent in multi-branch topologies, resulting in significant latency during inference. Additionally, existing methods share the same positive/negative sample set between the classification and localization branches, leading to sample misalignment, and rely solely on intersection-over-union for sample assignment, thereby causing a decrease in detection performance. To tackle these issues, this paper introduces a two-stage re-parameterization and sample disentanglement network (TRSD-Net). It is based on two-stage depthwise to pointwise re-parameterization (RepTDP) and task-aligned sample disentanglement (TSD). RepTDP employs structural re-parameterization to decouple the multi-branch topology during training and the plain architecture during inference, thus achieving low latency. By employing different sample assignment strategies, TSD can adaptively select the most suitable positive/negative sample sets for classification and localization tasks, thereby enhancing detection performance. Additionally, TSD considers three important factors influencing sample assignment. TRSD-Net is evaluated on both the UA-DETRAC and COCO datasets. On the UA-DETRAC dataset, compared to state-of-the-art methods, TRSD-Net improves the detection accuracy from 58.8% to 59.7%. Additionally, it reduces the parameter count by 87%, the computational complexity by 85%, and the latency by 39%, while increasing the detection speed by 65%. Similar performance improvement trends are observed on the COCO dataset.