基于数据集构建和改进BiSeNetV2的矿山道路检测研究

    Mining road detection based on dataset construction and improvement of BiSeNetV2

    • 摘要: 自动检测和识别矿山道路不仅能够提高矿山运输安全,而且可以提高生产效率。为了对矿山道路进行自动检测,研究选取了某实验矿山,使用专业设备采集了矿山区域的道路图像。随后通过直方图均衡化来提升图片质量,并引入中值滤波来进行数据去噪处理,以构建检测数据集。在检测模型上,研究采用了双边分割网络模型,并从三个方面对其进行了改进,即细节分支、语义分支和特征恢复。研究结果显示,检测模型的准确率最大值为97.82%,比四种对比模型的最大值分别高了10.63%、7.07%、6.17%和5.55%。该模型的F1最大值为0.993,综合性能较好。Dice系数最大值为0.975,更接近于1,且均方误差和平均绝对误差的平均值分别为1.204和1.110。此外,在计算消耗上,该模型的耗时、中央处理器利用率和内存占用率的最大值分别为83 ms、11.37%和10.95%,明显优于对比算法。研究设计的矿山道路检测模型具有良好的性能,能够对矿山行驶卡车提供道路识别和指引的技术支持。

       

      Abstract: Automatic detection and recognition of mining roads can not only improve mining transportation safety, but also enhance production efficiency. In order to automatically detect mining roads, an experimental mine is selected for the study, and professional equipment is used to collect road images of the mining area. Subsequently, histogram equalization is used to improve image quality, and median filtering is introduced for data denoising to construct a detection dataset. In the detection model, a bilateral segmentation network model is adopted and improved in three aspects, namely detail branch, semantic branch, and feature recovery. The results show that the maximum accuracy of the detection model is 97.82%, which is 10.63%, 7.07%, 6.17%, and 5.55% higher than the maximum values of the four comparison models, respectively. The maximum F1 value of this model is 0.993, indicating good overall performance. The maximum Dice coefficient is 0.975, which is closer to 1, and the average mean square error and mean absolute error are 1.204 and 1.110, respectively. In addition, in terms of computational consumption, the maximum values of the model’s time consumption, CPU utilization, and memory occupancy are 83 ms, 11.37%, and 10.95%, respectively, which are significantly better than the comparison algorithms. The mining road detection model designed for research has good performance and can provide technical support for road recognition and guidance for mining trucks.

       

    /

    返回文章
    返回