In the tree barrier clearance project for protecting the safety of the distribution network, the manual calculation of the felling quantity faces problems such as strong subjectivity of the calculation results and management difficulties. The existing algorithms have low accuracy, with many false positives and false negatives, and poor robustness. Therefore, a tree stump detection algorithm for calculating the felling workload in the transmission corridor tree barrier clearance is proposed. In response to the problem of inaccurate felling quantity calculation due to the complexity of the distribution network clearance scene and the difficulty in distinguishing between tree trunks and tree stumps, a feature extraction module based on Context Guide Block is designed. RepGFPN and Dysample structures are introduced to optimize the neck network, effectively integrating environmental context semantic information with local details of tree stumps. Subsequently, the algorithm designs a tree stump detection head based on LW-SEAM, optimizing the detection effect under occlusion. The model"s P, R, and mAP₅₀ indicators on the test set have been improved to 85.5%, 76.4%, and 80.4% respectively, showing good detection performance for tree stump detection in complex backgrounds and occlusion scenarios, and providing technical reference for achieving intelligent engineering calculation.