# Enhanced Real-Time Highway Object Detection for Construction Zone Safety Using YOLOv8s-MTAM

## TL;DR

This paper presents an improved object detection system for highway construction zones using YOLOv8s with a motion-temporal attention module, achieving high accuracy and real-time performance.

## Contribution

The novel motion-temporal attention module (MTAM) enhances detection of dynamic and occluded objects in high-speed environments.

## Key findings

- The system achieved mAP(IoU[0.5]) of 90.77% and mAP(IoU[0.5:0.95]) of 70.20% on a custom dataset.
- Real-world tests showed 96% recognition for construction vehicles and 92% for warning signs.
- The model is suitable for real-time deployment in intelligent transportation systems.

## Abstract

Reliable object detection is crucial for autonomous driving, particularly in highway construction zones where early hazard recognition ensures safety. This paper introduces an enhanced YOLOv8s-based detection system incorporating a motion-temporal attention module (MTAM) to improve robustness under high-speed and dynamic conditions. The proposed architecture integrates a cross-stage partial (CSP) backbone, feature pyramid network-path aggregation network (FPN-PAN) feature fusion, and advanced loss functions to achieve high accuracy and temporal consistency. MTAM leverages temporal convolutions and attention mechanisms to capture motion cues, enabling effective detection of blurred or partially occluded objects. A custom dataset of 34,240 images, expanded through extensive data augmentation and 9-Mosaic transformations, is used for training. Experimental results demonstrate strong performance with mAP(IoU[0.5]) of 90.77 ± 0.68% and mAP(IoU[0.5:0.95]) of 70.20 ± 0.33%. Real-world highway tests confirm recognition rates of 96% for construction vehicles, 92% for roadside warning signs, and 84% for flag bearers. The results validate the framework’s suitability for real-time deployment in intelligent transportation systems.