基于实例分割模型优化的道路抛洒物检测算法

在交通安全领域，道路抛洒物易引发交通事故，构成了交通安全隐患。针对传统抛洒物检测方式识别率低、对于多类抛洒物检测效果不佳等问题，提出了一种基于实例分割模型CenterMask优化的道路抛洒物检测算法。首先，使用空洞卷积优化的残差网络ResNet50作为主干神经网络来提取特征并进行多尺度处理；然后，通过距离交并比（DIoU）函数优化的全卷积单阶段（FCOS）目标检测器实现对抛洒物的检测和分类；最后，使用空间注意力引导掩膜作为掩膜分割分支来实现对于目标形态的分割，并采用迁移学习的方式实现模型的训练。实验结果表明，所提算法对于抛洒物目标的检测率为94.82%，相较常见实例分割算法Mask R-CNN，所提的道路抛洒物检测算法在边界框检测上的平均精度（AP）提高了8.10个百分点。

Road abandoned object detection algorithm based on optimized instance segmentation model

In the field of traffic safety， the road abandoned objects easily cause traffic accidents and become potential traffic safety hazards. Focusing on the problems of low recognition rate and poor detection effect for different abandoned objects of traditional road abandoned object detection methods， a road abandoned object detection algorithm based on the optimized instance segmentation model CenterMask was proposed. Firstly， the residual network ResNet50 optimized by dilated convolution was used as the backbone neural network to extract image features and carry out the multi-scale processing. Then， the Fully Convolutional One-Stage （FCOS） target detector optimized by Distance Intersection over Union （DIoU） function was used to realize the detection and classification of road abandoned objects. Finally， the spatial attention-guided mask was used as the mask segmentation branch to realize the object shape segmentation， and the model training was realized by the transfer learning method. Experimental results show that， the detection rate of the proposed algorithm for road abandoned objects is 94.82%， and compared with the common instance segmentation algorithm Mask Region-Convolutional Neural Network （Mask R-CNN）， the proposed road abandoned object detection algorithm has the Average Precision （AP） increased by 8.10 percentage points in bounding box detection.