Electric shovel is a large mechanical excavation equipment widely used in surface mining. During the excavation process, the prolonged direct impact of the shovel teeth against the ore can cause the shovel teeth to loosen or even break prematurely, resulting in unplanned downtime and lost productivity of the shovel. To solve this problem, an electric shovel tooth break detection method based on the improved YOLOX is proposed. This method is based on YOLOX. Firstly, for the problem of poor detection effect caused by uneven illumination, the dilated convolution attention mechanism is added to the feature pyramid network to enhance the saliency of the target in the complex background; Secondly, the corner efficient intersection over union(CEIOU) loss function is used to replace the original network loss function to optimize the network training process, thereby improving the detection accuracy of the target; Finally, considering the computing power of the embedded device itself, the model compression strategy is used to tailor the redundant channels in the network to reduce the model volume and improve the detection speed. The performance test is carried out on the self-built 4 200 WK- 10 electric shovel data set. The experimental results show that compared with the YOLOX network model, the average detection accuracy of the improved model reaches 95. 37%, which is 1. 95% higher, the detection speed is 46. 1 fps, an increase of 8. 4 fps, and the model size is 31. 74 MB, which is reduced to 32. 9% of the original. Compared with many other existing methods, the designed target detection algorithm has the advantages of high precision, small size and fast speed.