正确识别林区原木运输车辆能有效防止原木被违法运输的异常行为,提高监控管理森林资源的效力。为了解决由于林区道路场景的复杂性,原木端面颜色受光照、湿度等影响使得原木运输车辆识别率较低的问题,将基于YCbCr颜色空间和Hough变换圆检测相结合来识别林区原木运输车辆。同一捆原木端面颜色差异较小,可使用YCbCr颜色特征空间来分割图像,去除背景干扰;图像被转换到RGB空间以去除原木区域二值图像的背景;利用形态学方法统一去除二值化图像的原木缝隙来确定图像边缘;利用Hough变换圆的点线间的对偶性来检测原木运输车辆,降低了噪声的敏感性。实验结果表明,上述方法对成捆裸露在外的原木运输车辆识别率达到了71%以上,鲁棒性和有效性较好。

The research on forest log transport vehicle identification can effectively prevent the abnormal behavior of illegal transportation and improve the effectiveness of forest resource monitoring and management.However,the complex road scene in the forest area and the color of the log end face are susceptible to light,humidity,etc.,which increases the difficulty of the log vehicle identification.This paper proposes a forest vehicle identification method based on YCbCr color space and Hough transform circle detection.Considering a bundle of logs has the same background and small differences in color,using brightness and color features to segment the image into a YCbCr space with an excellent real-time performance to remove background interference.The image is reconstructed to the RGB space to obtain the binary image of the log area with the background removed.The morphological method is used to uniformly remove log gaps and filter the interference pixels to determine the edge of the image accurately.The duality between the dots and lines of the Hough transform circle is used to detect the log transport vehicle,and to reduce the sensitivity of noise.The experimental results show that the recognition accuracy rate for the bundle of bare log vehicles reaches more than 71%,and the identification method has good robustness and practicability.