基于多尺度联合权重分配的目标检测算法

针对SSD(single shot multibox detector)算法在复杂交通场景下对交通标志小目标识别效果不佳的问题，提出一种基于多尺度特征互补和重点特征信息挖掘（multi-scale feature complementary fusion and key feature information mining SSD,MK-SSD）的目标检测改进算法。利用跨阶段局部网络设计多尺度特征互补模块，同时构建多路径特征融合网络，有效提升浅层网络对小目标的特征提取能力。设计联合权重分配模块，将感知域与重点信息挖掘相结合，更高效地利用重点特征信息并抑制对非重点信息的关注度。利用轻量化残差块对预测网络进行改进，提升目标检测能力。经实验分析，改进后的算法在自制交通标志数据集上平均准确率达到89.64%，在保证实时性的同时，相较于YOLO系列和SSD系列算法拥有更高的检测精度，能检测出大部分SSD网络漏检的小目标。

Target Detection Algorithm Based on Multi-Scale Combined Weight Distribution

For the problem of SSD（single shot multibox detector） identification in complex traffic environments, this paper proposes a target detection improvement algorithm based on multi-scale feature complementarity and combined weight distribution（multi-scale feature complementary fusion and key feature information mining SSD, MK-SSD）. The proposed algorithm first designs multi-scale feature complementary modules using cross stage partial network and builds multi-path feature fusion networks to effectively improve the feature extraction capability of shallow networks for small targets. Secondly, the combined weight distribution module is designed to combine the perceptual domain with the key information mining to more efficiently use the key feature information and suppress the attention of the non-key information. Finally, the prediction network is improved using lightweight residual blocks to improve the target detection capability. After experimental analysis, the average accuracy of the improved algorithm reaches 89.64% on the homemade traffic sign dataset. While ensuring real-time, it has higher detection accuracy compared with YOLO series and SSD series algorithms, and can detect small targets of most SSD network leakage detection.