Accurate identification of surface cracks is of great significance for the site selection of transmission towers, as it directly affects the safety and stability of power grid construction. Traditional manual inspection methods are labor-intensive and inefficient, making them inadequate for large-scale and high-precision applications. Consequently, intelligent crack recognition technologies are receiving increasing attention and adoption. This study proposes a novel surface crack identification method that integrates multiple neural networks, aiming to overcome the limitations of traditional crack identification approaches, such as low accuracy and poor generalization. The proposed framework incorporates a convolutional neural network (CNN)-based data filtering module and a crack segmentation module combining UNet and YOLOv8 architectures. Together, these components form a robust and accurate end-to-end crack identification system, which is further applied to the evaluation of rock fragmentation. To verify the effectiveness and accuracy of the proposed method, experiments were conducted on a rock crack dataset and compared against several existing approaches. The results demonstrate that the proposed method achieves superior performance in crack detection accuracy. Moreover, tests on various scenario datasets also yielded promising results, indicating the strong generalization ability and adaptability of the method.