基于感受野注意力卷积的自动驾驶多任务感知算法OA北大核心CSTPCD

The critical components of autonomous driving perception,including drivable area segmentation,lane detec-tion,and traffic target detection,are executed concurrently,imposing substantial computational demands on intelligent vehicles.A balance between accuracy and speed in practical applications is achieved through the utilization of multi-task perception algorithms.Difficulties inherent in multi-task perception algorithms,such as complex road conditions and obscured targets,are addressed by proposing a multi-task perception algorithm based on receptive-field attention convolu-tion(RFAConv)through YOLOP network enhancement.Initially,certain convolutions in the backbone network are substi-tuted with receptive-field attention convolutions,dynamically allocating convolution kernel weights based on the impor-tance of image features within the receptive field to enhance the network's feature extraction capability.Subsequently,the feature pyramid network is reconstructed by replacing the original cross-stage hierarchical module with an efficient cross-scale fusion module to fully retain effective information during feature fusion.Additionally,a content-aware feature recombi-nation module is employed as an up-sampling method to mitigate information loss during feature fusion upsampling.Finally,the MPDIoU function is utilized to compute the regression loss,addressing issues related to differently sized but proportionate actual and predicted boxes,further enhancing the detection capability for traffic targets.Testing results on the BDD100K dataset demonstrate that the model,compared to other multi-task models and even single-task models,exhibits superior detection accuracy for drivable area segmentation,lane detection,and traffic target detection while con-currently maintaining real-time inference performance of the network.