SSH-YOLO: YOLOv8 improved model based on Object Detection in Complex Road Scenes

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: 12.4% improvement on RoadScene-Complex dataset (0.729 vs baseline)

• Primary Outcome: Object detection accuracy measured by [email protected] across multiple datasets

• Nnt Or Sensitivity: Inference speed: 60 FPS, suitable for real-time applications

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility given maintained lightweight characteristics and real-time performance Relevant for Australian smart city initiatives, autonomous vehicle trials, and traffic management systems under development by state transport authorities Autonomous vehicle systems, traffic monitoring infrastructure, and intelligent transportation systems

Abstract

To address the problem of insufficient detection accuracy for dense targets, small targets and partially occluded objects in complex road scenarios, an improved object detection model, SSH-YOLO, is proposed. On the basis of YOLOv8n, the model optimizes and improves performance through a three-level collaborative architecture: 1) introduce the spatial and deep conversion (SPDConv) module in the backbone network to replace the traditional step downsampling with nonstep convolution, retain the fine-grained features of small targets, and solve the feature loss problem of low-resolution images; 2) embed the spatial and channel collaborative attention module (SCSA), through cross-scale feature fusion (SMSA) and channel weight progressive optimization (PCSA), focus on the key visible areas of the occluded target and suppress background interference such as roadside vegetation; and 3) add a new 160 × 160 resolution small object detection head, combined with the original P3‒P5 layer to form a four-level detection system, covering long-distance small targets < 32 × 32 pixels. The experimental results show that the improved model performs well on the self-built RoadScene-Complex dataset and four public datasets BDD100K: 0.729 (12.4% higher than YOLOv8n) on the RoadScene-Complex dataset [email protected] (12.4% higher than YOLOv8n) and 0.868 (7.6% higher than the KITTI dataset) [email protected]). COCO small target subset [email protected] to 0.585 (up 16.5%), and CityPersons occluded scene [email protected] to 0.739 (up 22.8%). At the same time, it maintains lightweight characteristics and has an inference speed of up to 60 FPS to meet the needs of real-time on-board detection. The research results provide a balanced solution of "accuracy-speed-lightweight" for high-precision target detection in complex traffic scenarios, especially in small target and occlusion scenarios.

References

1. 1.Ma, T., Chen, Y., Li, J., Yu, H., & He, C. (2024). SSH-YOLO: YOLOv8 improved model based on Object Detection in Complex Road Scenes. PLoS One. https://doi.org/10.1371/journal.pone.0343924