Intelligent detection method of lightweight blasthole based on deep learning

Abstract

In the construction process of tunnel (roadway) drilling and blasting, intelligent charging can replace manual operation and reduce the occurrence of dangerous accidents in charging operation. However, some factors such as poor light conditions in the tunnel, small blasthole targets, and cracks in the tunnel face will cause the misdetection and missed detection of blastholes during intelligent charging. At the same time, the limited computing power of the vehicle-mounted computer is also a difficulty that restricts the use of large models for blasthole identification. The MCIW-2 deep learning model can solve the problem of high-precision blasthole detection and real-time deployment in the tunnel excavation environment. According to the size characteristics of the collected blasthole images, the model adopts the adaptive anchor frame clustering algorithm module to optimize the aspect ratio size parameters of the detection frame. The loss function WIoU (Wise Intersection over Union) with a dynamic non-monotonic focusing mechanism is used to deal with the challenge of low-quality blasthole images for achieving a high-precision detection. The MobileNetv3-Small network and CBAM (Convolutional Block Attention Module) are used to build a backbone network structure, reducing model parameters to ensure detection accuracy and meet the lightweight deployment requirements of vehicle equipment. Experiments have proved that the MCIW-2 model has reached 96.18% accuracy in blasthole recognition, and the detection speed has reached 59 fps. Compared with the benchmark YOLO (You Only Look Once) series target detection model with the smallest file, the lightweight blasthole intelligent detection model constructed is reduced by 75.86%, and the model file is only 2.80 Mb, which is better than the benchmark target detection model of the YOLO series. The MCIW-2 deep learning model is used to test the live video of the working face, and the rapid and accurate detection of blasthole is realized. The test results show that the model is suitable for the lightweight deployment requirements of intelligent charge engineering, has a good adaptability, and some significant advantages in comprehensive performance.

Keywords

• blasthole detection
• lightweight model
• object detection
• deep learning