Research on occluded vehicle detection algorithm based on improved SSD
In order to solve the problem of poor detection ability of occluded vehicle targets in complex environment,an improved SSD(Single Shot MultiBox Detector)based vehicle target detection algorithm was proposed.The backbone network was replaced with ResNet50 network,and the idea of residual learning was used to obtain better model training effect.In order to improve the detection ability of occluded vehicle target,the feature fusion module was introduced to increase the semantic information of the network and the attention mechanism was added to emphasize important features.The Focal loss function was used to solve the problem of positive and negative sample imbalance during model training.The experimental results showed that the proposed algorithm has higher detection accuracy and better detection effect on partially obscured vehicle targets.
deep learningoccluded vehiclefeature fusionattention mechanism
NETL
NSTL
万方数据
