# Intelligent Blashhole Detection of Roadway Working Face Based on Improved YOLOv7 Network
###### Abstract  
Blashhole detection is crucial but challenging in tedious underground mining processes, given the diversity of surrounding rock backgrounds and uneven light intensity. However, existing algorithms have limitations in extracting image features and identifying differently sized objects. This study proposes a cascade-network-based blashhole detection method. The proposed method includes a blashhole feature extract transformation (BFET) module and a blashhole detection (BD) module. Firstly, we constructed the BFET module on the improved Cycle Generative Adversarial Network (CycleGAN) by multi-scale feature fusion. Then, we fused the convolution features of the generators in CycleGAN to obtain the enhanced feature map of the blashhole images. Secondly, the BD module was cascaded with the BFET module to accomplish the task of detecting blashholes. Results indicated that the detection accuracy of the blashhole image was significantly improved by strengthening the contrast of the image and suppressing over-exposure. The experimental results also showed that the proposed method enhanced the contrast of the image and could improve the accuracy of blashhole detection in real time. Compared with the YOLOv7 and CycleGAN+YOLOv7 methods, the detection accuracy of our method was improved by 5.34% and 2.38%, respectively.  
blashhole detection; object detection; feature extraction; Generative Adversarial Network +
Footnote †: journal: Medical and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China  
roadway working faces. Therefore, this paper proposes a method to enhance the contrast of images by enhancing the image's blasthole features, according to the characteristics of the blasthole images.  
Traditional detection algorithms struggle to accurately detect blastholes in roadway working faces due to interference from surrounding rock, low illumination and coal dust. To improve the accuracy of blasthole detection, this paper proposes a cascade detection network called the feature extract transformation network for blasthole detection (BFET-BD). The contributions of this paper mainly include the following.  
(1) To reduce the impact of the surrounding rock on blasthole images and generate high-contrast blasthole feature maps, the blasthole feature extract transformation (BFET) module and the blastholes detection (BD) module are proposed. The BFET module utilizes features from different convolutional layers in CycleGAN for multi-scale feature fusion to generate enhanced feature maps. The BD module is built on existing single-scale object detection networks. The output feature map of the BFET module is used as input to the BD module to form an end-to-end cascaded blasthole detection network.  
(2) To address the interference issue arising from low-illumination conditions, this paper suggests utilizing multi-scale channels rather than three-channel images. This approach enhances the features of blastholes in low-illumination environments, thereby improving the detection accuracy and reducing false detection rates.  
(3) To address the lack of available blasthole datasets, this study collects a large number of blasthole images from various roadway working faces and establishes the CUMTB-BI dataset. Model training and testing are conducted on this dataset, providing a foundation for the verification of the proposed blasthole detection network.  
The remainder of this paper is organized as follows: the recent literature is reviewed in Section 2; Section 3 describes the proposed method in detail; Section 4 provides a detailed account of the dataset collection and training process; Section 5 reports the experiments and results; and Section 6 concludes the work.