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.