Abstract: In view of problems that the existing underground drill pipe quantity statistics method has low accuracy and is easily affected by environmental changes,an underground drill pipe counting method based on improved ResNet network was proposed combining with convolutional neural network, signal filtering and other technologies. According to the difference between unloading action and non-unloading action in video image, the sample set is classified and trained based on the ResNet-50 network model to determine whether each frame of the video contains the unloading action; linear learning rate preheating and Logistic-based learning rate attenuation strategy are combined to update learning rate and improve the accuracy of model classification; the video classification confidence is filtered through the integration method, and the number of falling edges of the confidence curve is counted to realize drill pipe counting. Experimental results show that the preheating + attenuation learning rate update strategy can effectively improve classification accuracy of the image classification model to 89%. The actual application results show that underground drill pipe counting method based on improved ResNet network can efficiently identify unloaded rod images in the video with an average accuracy of 97%, which meets the actual application requirements.