In order to realize the accurate measurement of drilling depth of directional drilling rig under coal mine, a lightweight drilling rig target detection network integrating attention mechanism and inverse residual structure ( GCI-YOLOv4) is proposed. Through automatic, rapid and accurate detection, the movement track of drilling rig, the number of driven drill rods and the drilling depth are obtained. Aiming at the problem of low color gamut discrimination in coal mine, GhostNet is used as the feature extraction network to remove the redundant features of complex background, lighten the model and accelerate the speed of model reasoning. Aiming at the problem of low target saliency of drilling rig caused by uneven illumination in coal mine, the attention module is introduced to enhance the saliency of drilling rig in complex background. Aiming at the problem that it is difficult to detect accurately when the drilling rig moves at high speed, the inverse residual structure is introduced to extract richer semantic features while maintaining the balance between speed and accuracy. In order to ensure the accuracy and reliability of the model, the proposed detection algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed detection algorithm can better solve the problems of low background gamut discrimination, high-speed movement of drilling rig and uneven illumination under coal mine. The average detection accuracy is 99. 49% and the detection speed is 58. 10 FPS. The performance is better than the classical target detection algorithm. The proposed detection algorithm is deployed in the field of the working face for testing, which can accurately obtain the motion trajectory of the drilling rig. The number of drill pipes is calculated by filtering and counting the rising edge. The counting accuracy of drill pipes is 99. 4%. The drilling depth is accurately calculated, which verifies the feasibility and practicability of this method.