| Summary: | Blasthole 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 blasthole detection method. The proposed method includes a blasthole feature extract transformation (BFET) module and a blasthole 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 blasthole images. Secondly, the BD module was cascaded with the BFET module to accomplish the task of detecting blastholes. Results indicated that the detection accuracy of the blasthole 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 blasthole 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.
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