This paper proposes an improved YOLO6D based target attitude estimation algorithm to address the problem of difficulty in accurately estimating the pose of occluded and weakly textured targets in three-dimensional space.Firstly,the residual network structure was introduced to solve the gradient problem caused by the increase in neural network layers and accelerate model convergence;Secondly,the addition of the spatial pyramid pooling(SPP-CSP)module enables the network to fully utilize multi-scale feature map information to enhance feature extraction of the target object.The experimental results show that the im-proved network has an overall increase of 6.68%in 2D reprojection and 6.05%in 5 cm5°on the self built dataset,and an overall accuracy increase of 8.74%on the official dataset Occlusion LineMOD,effectively improving the overall detection performance of target attitude estimation.
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