基于自适应融合的实时车辆检测OA北大核心CSTPCD

A traffic target detection algorithm,fusion attention adaptive pyramid network(FAAP-Net),is proposed to address the issues of slow speed and low accuracy in traditional vehicle detection techniques,significantly reducing the occurrence of traffic accidents.To mitigate computational complexity,a lightweight complementary pooling structure(CPS)is designed,employing two sets of different pooling combinations in width and height,which maintains a high precision while significantly reducing the floating point operations per second(GFLOPs)and the parameter count of the network.Addressing the information loss during intelligent traffic system feature map generation,the adaptive fusion feature pyramid network(AF-FPN)incorporates the adaptive attention module(AAM)and the feature enhancement module(FEM)to integrate shape features for vehicle detection.Lastly,to address the weak representation of vehicle detail features,a channel-wise grouped attention(SA)mechanism is introduced,enhancing the focus of the backbone network on various vehicle detection details and effectively extracting significant features.The experimental results on the BDD100K dataset demonstrate that the FAAP-Net algorithm achieves a notable improvement,increasing the average precision from 30.3%to 43.7%.