College of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2022 Nov 7;22(21):8577. doi: 10.3390/s22218577.
With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.
随着人工智能和计算机视觉技术的不断发展，自动驾驶汽车发展迅速。虽然自动驾驶汽车在正常环境下已经取得了很好的效果，但在恶劣天气下行驶仍然对驾驶安全构成挑战。为了提高自动驾驶车辆在恶劣环境下的检测能力，我们首先构建了一个新的颜色级别偏移补偿模型，对图像进行自适应颜色级别校正，这可以有效地提高恶劣天气下目标的清晰度，便于目标的检测和识别。然后，我们比较了几种常见的单阶段目标检测算法，并对表现最好的 YOLOv5 算法进行了改进。我们通过增加模型参数数量并将 Transformer 和 CBAM 引入 YOLOv5 算法，对 YOLOv5 算法的 Backbone 参数进行了优化。同时，我们使用 EIOU 损失函数替换了原始 CIOU 的损失函数。最后，通过消融实验比较，改进后的算法提高了目标的检测率，mAP 达到 94.7%，FPS 达到 199.86。
