Vehicle Target Detection Model Based on CBAM-BiFPN and Improved CenterNet Coding

The study introduces a new method to enhance vehicle type recognition rates in Internet of Vehicles environment. The approach integrates a vehicle target detection model that utilizes bidirectional feature fusion of a hybrid attention mechanism and an enhanced CenterNet encoding technique, with ResNet18 as the base network. By decoupling detection and classification processes, the model focuses on vehicle characteristics and unique model differences, boosting accuracy. Additionally, Scale feature information is incorporated to improve CenterNet vehicle target detection by learning width, height, and shape details. To address low detection rates of specific vehicle models like buses and vans, a bidirectional feature fusion mechanism is employed, combining a hybrid attention mechanism (CBAMBiFPN) to enhance feature utilization and detection accuracy. Experimental results on UA-DETRAC and BDD datasets demonstrated an average accuracy increase, validating the model’s effectiveness. Compared to the original model, the new model showed improvements in mean average precision, F1-Score, and detection speed. Specifically, the UA-DETRAC data set saw a 1.6 percentage point increase in mean average precision and a 1.8 percentage point increase in F1-Score, with a detection speed of 68 frames/s. On the BDD100K data set, the model improved mean average precision by 1.1 percentage points. The study showcases enhanced accuracy without compromising real-time performance.