Rock fracture detection and identification in outcrop areas via improved YOLOv7

The detection of rock fractures in outcrop areas plays a pivotal role in the geological exploration of fracture-type hydrocarbon reservoirs, an essential aspect of the energy industry. Traditional image processing methods for rock fracture detection have faced limitations in providing precise results. Furthermore, the application of traditional deep learning models for rock fracture detection in complex geological settings have a low computing efficiency and accuracy.

To meet these challenges, this paper introduces an advanced and innovative rock fracture detection algorithm, known as YOLOv7-PCN. It represents a significant advancement in the field of geological exploration by improving in both accuracy and efficiency during rock fracture detection. The YOLOv7-PCN algorithm incorporates several improvements to enhance its performance. First, the PConv (Partial Convolution) module were included to replace conventional convolutions within the network backbone, which dramatically reduces computational complexity and improve the enhanced detection speed. Second, YOLOv7-PCN introduces the Coordinate Attention (CA) mechanism, a critical addition that refines the extraction of vital edge and densely-distributed features associated with fractures. It can detect and localize fractures with unprecedented accuracy, even in complex geological backgrounds. An instrumental advantage of this algorithm is the integration of the Normalized Wasserstein Distance (NWD) measurement, which are used as the bounding box regression loss function. It significantly optimizes the training convergence, leading to further improvement on localization and detection accuracy, especially for small target fractures. Notably, YOLOv7-PCN is suitable for the scenarios where rock images exhibit low resolution and complex geological contexts. To improve the model's adaptability to various datasets, YOLOv7-PCN incorporates the data augmentation into the data preprocessing pipeline.

The experimental results validate the remarkable performance of YOLOv7-PCN, which attains an impressive mAP (mean Average Precision) score of 82.5% in the detection of fractures among four distinct rock categories. This achievement represents a substantial increase in accuracy, with 7.7% improvement compared to the original YOLOv7 algorithm. Remarkably, YOLOv7-PCN accomplishes these advancements while significantly reducing the number of model parameters by 29.6%, with 31.2% computational resources being saved. Furthermore, it also stands out for its exceptional detection speed, with 39.2% being increased. In summary, the YOLOv7-PCN rock fracture detection algorithm represents a transformative milestone in the realm of geological exploration. It balances the relationship between lightweight modeling and detection accuracy enhancement, which is suitable for deployment in complex geological environments. This innovation not only provides a crucial technological reference for the identification and exploration of geological rock fractures, but also makes progesses in the geological exploration of fracture-type hydrocarbon reservoirs.

Moreover, YOLOv7-PCN can accelerate and improve the precision of fracture detection, thereby contributing to geological studies and resource assessment.