Multi-target vehicle detection based on corner pooling with attention mechanism

Multi-target detection based on corner pooling provides a distinctive framework without anchor boxes, which has achieved wide application in the area of intelligent transportation system. To effectively detect small vehicles in the distant view, we propose an improved detection network termed corner pooling with attention mechanism (CPAM). A newly devised network called Hourglass with Coordinate Attention(Hourglass-CA) is proposed as an alternative to the Hourglass-104 backbone network. This one incorporates a multi-level attention mechanism to optimize the efficiency of feature extraction. Additionally, a novel multi-level attention loss(MLA loss) is presented, which dynamically adjusts the offsets during the feature extraction process. The experimental results demonstrate that our proposed CPAM achieves lightweight detection, reducing the parameters from 201M to 117M with an FPS from 4.2 to 16.1. Moreover, the AP can reach 51.6%, surpassing several existing detectors.