To address the problem that existing attitude estimation algorithms are not effective in detecting small target pedestrians in an urban streetscape,this study proposes a pose estimation algorithm for small target pedestrian,YOLO-Pose-CBAM,based on YOLO-Pose.First,the CBAM attention mechanism module is introduced to enhance the ability of the network to focus on small target pedestrian areas and improve the sensitivity of the algorithm to small target pedestrians on the premise of not increasing the computation excessively.Simultaneously,four detection heads of different sizes are used in the trunk network to enrich the detection means of the algorithm for pedestrians of different sizes.Second,two cross layer cascading channels are constructed between the Backbone and Neck,which improves the feature fusion ability between the shallow and deep networks,further enhancing the information exchange and reducing the missed rate of small target pedestrians.Furthermore,the SIoU is introduced to redefine the location loss function of the boundary box regression,which can accelerate the convergence speed of the training and improve the detection accuracy.Finally,the k-means++algorithm is used instead of the k-means algorithm to cluster the tagged anchor frames in the dataset,avoiding the local optimal solution problem caused by the initialization of the clustering center to select the anchor frame that is more suitable for detecting small target pedestrians.Compared with the experimental results,the Average Precision(AP)of the proposed algorithm for the small target pedestrian WiderKeypoints dataset is improved by 4.6 percentage points compared with that of YOLO-Pose and by 6.5 percentage points compared with that of YOLOv7-Pose.
YOLO-Pose algorithmpose estimationcross layer cascadingCBAM attention mechanismSIoU loss functionk-means++algorithm
维普
万方数据
