The accurate identification and real-time detection of obstacles have been considered the premise to ensure the safe operation of coal mine driverless electric locomotives. The harsh coal mine roadway environment leads to low detection accuracy of obstacles based on traditional detection methods such as LiDAR and machine learning, and these traditional obstacle detection methods lead to slower detection speeds due to excessive computational reasoning. To address the above-mentioned problems, we propose a deep learning-based ODEL-YOLOv5s detection model based on the conventional YOLOv5s. In this work, several data augmentation methods are introduced to increase the diversity of obstacle features in the dataset images. An attention mechanism is introduced to the neck of the model to improve the focus of the model on obstacle features. The three-scale prediction of the model is increased to a four-scale prediction to improve the detection ability of the model for small obstacles. We also optimize the localization loss function and non-maximum suppression method of the model to improve the regression accuracy and reduce the redundancy of the prediction boxes. The experimental results show that the mean average precision (mAP) of the proposed ODEL-YOLOv5s model is increased from 95.2 to 98.9% compared to the conventional YOLOv5s, the average precision of small obstacle rock is increased from 89.2 to 97.9%, the detection speed of the model is 60.2 FPS, and it has better detection performance compared with other detection models, which can provide technical support for obstacle identification and real-time detection of coal mine driverless electric locomotives.
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