Abstract: [Objective]The detection of rock fractures in the outcrop area is of great significance for the geological exploration of fractured oil and gas reservoirs, but the traditional image processing algorithm is not effective for the detection of rock fractures, the classical depth learning model is characterized by high complexity, slow convergence and detection speed, and low detection accuracy for rock fractures with complex geological background, therefore, this paper presents an improved outcrop rock fracture detection algorithm YOLOv7-PCN. [Methods]Firstly, PConv (Partial Convolution) Convolution module is used to replace the standard Convolution in the backbone network, resulting in reduced computational complexity and improved detection speed. Moreover, the integration of the Coordinate Attention (CA) mechanism further improves the feature extraction capability, especially for capturing key information from critical fracture locations. Finally, the bounding box regression loss function is measured by NWD (Normalized Wasserstein Distance), which optimizes the training convergence rate of the network, the location and detection accuracy of small target fracture with lower resolution in complex environment is improved. At the same time, in data processing, A dataset of rock fractures in outcrop areas is created using data augmentation techniques to enhance the generalization capability of the network model. [Results]Experimental findings reveal an impressive average accuracy of 82.5%, surpassing the original YOLOv7 algorithm by 7.7%. Furthermore, the model's parameters are significantly reduced by 29.6%, resulting in a 31.2% reduction in computational workload. Additionally, the model's detection speed is impressively improved by 39.2%, which proves the effectiveness of the improved algorithm model. [Conclusion]The proposed enhanced YOLOv7 algorithm for rock fracture detection in this paper integrates the strengths of traditional convolutional neural network models with advanced deep learning-based target detection models, with fewer parameters and reduced computation. As a result, it achieves a higher level of accuracy in detecting rock fractures even in challenging and complex environments. The algorithm successfully meets the requirements for both accuracy and real-time performance in rock fracture detection tasks.