Abstract: As the core equipment in open-pit mining production, electric shovels are prone to problems such as gear sleeve detachment and breakage during operation. When the fallen parts mix into the ore, it can easily cause accidents such as crushing equipment jamming, damage, or belt tearing. To solve these problems, we propose a lightweight and high-precision model based on the improved YOLOv9-s model. By introducing the FasterNet backbone network, improving the AConv module in the neck network with DualConv (Dual Convolution), and re-lightweighting P-RepNCSPELAN4 with PConv (Partial Convolution), the proposed model's feature extraction ability is ensured while reducing the number of parameters and floating-point operations. The results show that the proposed reduces the weight by 36.2%, the number of parameters by 36.2%, the floating-point operation GFLOPs by 48.1%, and mAP@50 reaches 99%. The status detection method can satisfy the intelligent application need of open-pit mine electric shovel tooth detection.