Unified multi-task YOLO-Lite framework for road defect detection and image-level classification with edge-optimized quantization and explainable visual inference

An innovative unified framework named YOLO-Lite has been developed for simultaneously detecting road defects and classifying images. This lightweight multi-task architecture operates within a single end-to-end system, eliminating redundant computations and enhancing interaction between local and global representations. By utilizing a shared depthwise-separable convolutional backbone with residual refinement and specialized detection and classification heads, YOLO-Lite jointly optimizes objectness prediction, bounding-box localization, instance-level defect classification, and image-level categorization through a composite multi-task objective.

On the RDD test set, YOLO-Lite demonstrates impressive performance metrics, achieving an mAP@0.50 of 0.907 and an image-level classification accuracy of 0.921, while maintaining a relatively small model size of just 1.2 million parameters and requiring only 3.5 GFLOPs. In comparison to recent lightweight detectors, YOLO-Lite offers a superior balance between accuracy and computational efficiency.

To further validate the framework's effectiveness and generalization capability, extensive ablation, robustness, cross-dataset, and statistical analyses were conducted. Quantitative and qualitative Grad-CAM analyses confirm that the activation regions learned by YOLO-Lite align closely with annotated pavement defects.

For deployment in resource-constrained environments such as edge devices, YOLO-Lite incorporates INT8 quantization, which reduces model size and enhances inference speed with only a slight decrease in performance. Preliminary GPU-based measurements suggest that YOLO-Lite shows strong potential for future edge deployment, although physical embedded hardware validation is still required for definitive conclusions.

This research was supported by Postgraduate Doctoral Fellowship Funding received by M.I. from the University of Johannesburg. The study was conducted by researchers from various institutions, including the Department of Mathematics at the University of Johannesburg, King Saud University, and the University of Lahore. The work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, allowing non-commercial use and sharing with proper attribution to the original authors and source.