Height Limit Deep Detection Network Based on Key Points and Multi-Frame Image Feature Fusion
Road condition detection is a core task in intelligent driving,including height limit detection tasks.Considering that the research related to height limit de-tection in the academic community is not yet mature,we have conducted research on height limit detection methods and proposed a height limit detection network based on key points and multi-frame image feature fusion.By adopting key points in the height limit detection task,unnecessary predictions are reduced and detection effi-ciency is improved.By introducing a convolutional gated recurrent unit(ConvGRU)to model multiple images and learn the contextual relationship between multiple im-ages,improve recall rate,and reduce missed detection rate.The spatial particulars feature(SPF)module is proposed,which strengthens the multi-scale feature fusion in the decoding layer.In order to improve the accuracy of the model,the coordi-nate attention mechanism is introduced,and the target detection area is further paid attention to.According to the experimental results,this network can not only com-plete the height limit detection task well,but also balance the precision and recall rate better,with higher F1 values and fewer parameters compared with other advanced networks such as BiSeNet,PINet,PSPNet,etc;At the same time,in the task of lane line detection,it also performs excellently in terms of accuracy and missed detection rate,further proving the effectiveness of the network.
Deep learningkey pointsmulti-frame imageheight limit detectionintelligent drivingattention mechanism