Abstract: The existing deep learning based shovel detection methods fail to balance detection speed and precision well. In order to solve the above problem, an improved YOLOv7 model is proposed and applied to mining shovel detection. This model is based on the YOLOv7 model, using a lightweight GhostNet network for feature extraction in the backbone network. This model replaces some ordinary convolutions with lightweight GSConv in the neck network to reduce the number of model parameters and computation, and improve the detection speed of the model. Considering the impact of reduced model parameters on feature information extraction capability after lightweight improvement, the neck network is further improved without increasing computational complexity. The coordinate attention mechanism (CA) is embedded in the extended efficient layer aggregation network (ELAN). The bidirectional feature pyramid network (BiFPN) is used to improve path aggregation network (PANet) to enhance the network's capability to extract feature information. Furthermore, it effectively improves the precision of model detection. The experimental results show that compared with the YOLOv7 model, the improved YOLOv7 model reduces the number of parameters by 75.4%, reduces the number of floating-point operations per second by 82.9%, and improves the detection speed by 24.3%. Compared with other object detection models, the improved YOLOv7 model achieves a good balance between detection speed and precision, meeting the demand for real-time and accurate detection of electric shovels in open-pit coal mine scenarios. It provides favorable conditions for embedding into mobile devices.