多尺度特征融合的雾霾环境下车辆检测

针对雾霾环境下车辆检测准确率低、漏检严重的问题, 提出一种多尺度特征融合的雾霾环境下车辆检测算法. 首先利用条件生成对抗网络对雾霾图像进行去雾预处理, 然后针对雾霾环境下目标特征不明显的特点, 提出多尺度特征融合模块, 在YOLOv3的基础上, 从主干网络提取特征时增加一条浅层分支和深层特征进行上采样拼接融合, 得到尺度为104×104的特征图, 用于增强浅层的语义信息. 并采用CBAM注意力机制引导下的特征增强策略, 保证上下文信息的完整性, 以提高检测的精度, 最后将去雾后图片送入改进后的YOLOv3网络进行检测. 实验结果表明, 相较于原始网络, 该算法在RTTS数据集上的检测结果更加优秀, 模型可以达到81%的平均精度和67.52%的召回率, 能够更加精确的定位到车辆.

Multi-scale Feature Fusion for Vehicle Detection in Haze Environment

Given low vehicle detection accuracy and serious miss detection in a haze environment, a vehicle detection algorithm with multi-scale feature fusion in a haze environment is proposed. Firstly, the conditional generation and adversarial network is employed to preprocess the haze images. Then, as the object feature is not obvious in a haze environment, a multi-scale feature fusion module is put forward. On the basis of YOLOv3, a shallow branch is added for upsampling splicing and fusing with deep layer features during extracting features from backbone networks. As a result, the feature map with the scale of 104×104 is obtained, which is adopted to enhance the shallow semantic information. The feature enhancement strategy guided by the CBAM attention mechanism is utilized to ensure the integrity of context information and improve detection accuracy. Finally, the dehazed images are sent to the improved YOLOv3 network for detection. Experimental results show that the proposed algorithm has better performance than the YOLOv3 algorithm on the RTTS dataset. The proposed model can achieve an average accuracy of 81% and a recall of 67.52% and can locate vehicles more accurately.