Work

Pavement defect detection algorithm SSC-YOLO for fusing multiscale spatial channels in YOLOv8

Year: 2025

Type: article

Abstract: To address the challenges of poor detection performance and low computational efficiency in existing pavement defect detection models, a novel algorithm, You Only Look Once with Spatial Slicing Convolution (SSC-YOLO), is proposed. This method enhances detection accuracy by integrating multi-scale spatial feature channels into the You Only Look Once version 8 (YOLOv8) framework. Key improvements include the replacement of conventional downsampling and upsampling operations with Spatial Slicing Downsampling (SSD) and Multichannel Fusion Upsampling (MUP), respectively, which more effectively preserve original feature information. A multi-scale convolutional module with large kernels is also incorporated to expand the receptive field and improve model robustness. In addition, the Multi-scale Fusion Convolution (MFConv) mechanism is introduced to construct a parameter-sharing detection head, thereby improving accuracy while simultaneously reducing memory usage and model complexity. Experimental results on the VOC2007 dataset demonstrate substantial performance gains, with improvements of 2.1% in average recall (AR), 2.2% in mean average precision at 50% Intersection over Union (IoU) (mAP50), 2.9% in mAP75, 3.2% in mAP50–95, 3.0% in average precision for small objects (APS), 2.5% for medium objects (APM), and 3.7% for large objects (APL).

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.3187

Authors: Guangling Sun, Yanfei Jin

Institutions: Anhui Jianzhu University, Anhui Normal University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,395