Pedestrian detection algorithm of coal mine underground
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摘要: 针对井下光照不均匀、行人特征与背景的相似度高等导致基于计算机视觉的行人检测技术在井下应用面临很大挑战的问题，提出采用Faster区域卷积神经网络（RCNN）进行煤矿井下行人检测。Faster RCNN行人检测算法采用区域建议网络（RPN）生成候选区域，RPN 与Fast RCNN共享卷积层，以提高网络训练和检测速度；在图像特征提取过程中采用动态自适应池化方法对不同池化域进行自适应池化操作，提高了检测准确性。实验结果表明，该算法对于不同环境下图像中的行人均具有较好的检测效果。Abstract: Due to uneven underground illumination and high similarity between pedestrian characteristics and background, pedestrian detection technology based on computer vision is facing great challenges in underground application. Faster region convolutional neural networks(RCNN) was proposed for pedestrians detection of coal mine underground. Faster RCNN pedestrian detection algorithm uses region proposal network(RPN) to generate candidate regions. RPN shares convolutional layer with Fast RCNN, so as to improve network training and detection speed. A dynamic self-adaptive pooling method is adopted to perform self-adaptive pooling operation for different pooling domains in the process of image feature extraction, so as to improve detection accuracy. The experimental results show that the algorithm has better detection effect for pedestrian image in different environments.
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