基于DYCS-YOLOv8n的井下无人驾驶电机车多目标检测
Multi-target detection of underground unmanned electric locomotive based on DYCS-YOLOv8n
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摘要: 针对井下无人驾驶电机车因光线暗、粉尘多等因素导致图像特征难提取，细节易丢失，小尺寸目标难识别的问题，提出了一种基于DYCS-YOLOv8n的井下电机车图像多目标检测模型。首先，在原有YOLOv8n的基础上引入了CBAM模块，通过空间和通道双重注意力机制，提高了对关键特征的提取能力；其次，采用动态上采样算子DySample，更好地保留图像中的边缘和局部细节，提高检测的精确度；最后，新增小目标检测层，通过更好地提取细小特征，提升对小目标的检测性能。进行消融实验和对比实验，实验结果表明：DYCS—YOLOv8在自制数据集上的平均检测精确率(map)达到94.3%，较原模型提高了3.3%；小目标检测精度为89.2%，提高了6.3%；同时综合性能表现也优于其他主流目标检测模型。Abstract: In order to solve the problems of difficult to extract sample features, easy to lose image details and difficult to identify small-size targets due to factors such as dark light, excessive noise and motion blur of underground unmanned electric locomotive, a multi-target detection model of underground electric locomotive based on DYCS-YOLOv8n was proposed. Firstly, based on the original YOLOv8n, CBAM module is introduced to improve the ability to extract key features through the dual attention mechanism of space and channel. Secondly, the dynamic up-sampling operator DySample is used to better preserve the edges and local details in the image and improve the accuracy of detection. Finally, a new small target detection layer is added to improve the detection performance of small targets by better extracting fine features. The results of ablation and comparison experiments show that the average detection accuracy (map) of DYCS-YOLOV8 on the self-made data set is 94.3%, which is 3.4% higher than that of the original model. The detection accuracy of small target is 89.2%, which is improved by 6.3%. At the same time, the comprehensive performance is better than other mainstream target detection models.
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