Research on Multi-task Learning Algorithms for Pavement Spills Detection
To address the shortcomings of existing detection algorithms in detecting road-spill objects,a no-vel approach based on multi-task learning and image processing for road-spill object detection is proposed.Firstly,a semantic segmentation head that integrates multi-layer semantic features is designed for YOLOv6s,and loss functions are devised for both the object detection and semantic segmentation branches to enable multi-task learn-ing.Secondly,by leveraging the advantage of continuity in the semantic segmentation branch,coupled with im-age processing techniques,the complete road area is extracted.Finally,a method for identifying and excluding spilled object regions is introduced,which combines IoU and centroid position,enabling the exclusion of non-spilled objects and the final extraction of spilled object regions.Experimental results demonstrate that on the BDD100k dataset,the improved algorithm achieves a mean Average Precision(mAP)of 77.8%for vehicle ob-ject detection and a mean Intersection over Union(mIoU)of 91.5%for drivable area segmentation.Additionally,spilled objects of various scales can be accurately extracted.The algorithm can be directly deployed on vehicle-mounted equipment for existing road damage inspection tasks.
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