Research on Lightweight Road-Target-Recognition Algorithm in Complex Environment
Road-target recognition is a core technology used in intelligent transportation systems to solve urban congestion problems.However,existing algorithms exhibit unsatisfactory recognition performance in complex traffic environments,with numerous missed and false detections.Moreover,the model parameters are large,thus rendering them unsuitable for deployment on resource-limited mobile devices in practical scenarios.Hence,a lightweight road-target-recognition algorithm for complex environments is proposed in this study.A reconfigurable feature-extraction framework is designed based on the structure of the Single Shot Multi-Box Detector(SSD)algorithm.Three lightweight modules are used to construct shallow feature-extraction networks,and a custom Additional Block is used to construct deep-feature-extraction networks.The channel attention mechanism and a Lightweight Receptive Field Expansion(RFB-L)module are used to improve the detection performance of the model on targets of various sizes.Utilizing a custom pixel and a channel-information-fusion module to combine shallow and deep features enriches the information in the detection feature map.Meanwhile,a multi-feature,fusion learning-rate-adjustment algorithm is proposed to ensure the stable convergence of the model during training.A custom-developed dataset reflecting the complex and congested road of Hohhot_city is used to train and test the proposed algorithm.Comparative experimental results yielded by mainstream algorithms show that the proposed algorithm performs significantly better than YOLOv4-tiny and YOLOv5s algorithms under the same number of parameters.Its detection accuracy is similar to that of the YOLOv5m algorithm when the parameters are less than 40％.Additionally,its inference time and mean Average Precision(mAP)are 12.8 ms and 99.1％,respectively.
road-target recognitionfeature extractionfeature fusionchannel attentionreceptive field
万方数据
维普
