基于改进YOLOv4的综采工作面目标检测

Target detection of the fully mechanized working face based on improved YOLOv4

综采工作面关键设备及人员的准确检测是实现煤炭智能化开采信息感知的重要环节。传统目标检测算法通过人工提取特征实现目标检测,易受环境影响,不具有普适性。基于卷积神经网络的目标检测算法可以自适应地提取深层信息,但复杂环境下检测精度不高、网络参数多、计算量大。针对上述问题,提出了一种改进YOLOv4模型,并将其应用于综采工作面目标检测。为准确从综采工作面复杂环境中检测到目标,在CSPDarkNet53网络中融入残差自注意力模块,保证参数共享及高效局部信息聚合的同时增强全局信息获取能力,提升图像关键目标特征表达能力,进而提高目标检测精度;为适应综采工作面目标检测高效性需求,引入深度可分离卷积替代传统卷积,以减少模型参数量和计算量,有利于模型的工业部署,提高目标检测速度。实验结果表明,与YOLOv3、CenterNet及YOLOv4模型相比,改进YOLOv4模型平均精度均值最高,达92.59%,且在参数量、计算量、检测精度上具有更优的平衡,可在煤尘干扰、光照不均、目标运动等复杂环境下对目标准确检测。

The accurate detection of key equipment and personnel in the fully mechanized working face is an important link to realize the information perception of intelligent coal mining. The traditional target detection algorithm realizes the target detection by extracting the features manually. But it is easily affected by the environment and it is not universal. The target detection algorithm based on the convolutional neural network can extract deep information adaptively. But the detection precision is not high, the network parameters are too many,and the calculation is too large in complex environment. In order to the above problems, an improved YOLOv4model is proposed and applied to the target detection of the fully mechanized working face. In order to accurately detect targets in the complex environment of a fully mechanized working face, a residual self-attention module is integrated into the CSPDarkNet53 network. The capability of acquiring global information is enhanced while parameter sharing and efficient local information aggregation are ensured. The capability of expressing the features of key targets in an image is improved, and the target detection precision is further improved. In order to meet the requirement of high efficiency of target detection in the fully mechanized working face, depthwise-separable convolution is introduced to replace traditional convolution. The model parameter quantity and calculation quantity are reduced. It is beneficial to the industrial deployment of the model. And it improves target detection speed. The experimental results show that compared with YOLOv3, Center Net and YOLOv4 models,the average precision of the improved YOLOv4 model is the highest, up to 92.59%. It has better balance in parameter quantity, calculation quantity and detection precision. It can accurately detect the target in the complex environment such as coal dust interference, uneven lighting and motion blur.