Objective Engineering vehicles operating on large-scale construction sites in high-consequence areas pose severe safety hazards to buried pipelines. This paper addresses the shortcomings of current common techniques for detecting overlapped targets of engineering vehicles and target detection in scenarios with varying sunlight, highlighting issues such as high miss rates and low detection accuracies. The paper introduces a target detection method for engineering vehicles named YOLO-MMCE, which is based on an improved version of YOLOv5. This method focuses on recognizing four main types of engineering vehicles: excavators, loaders, rollers, and heavy trucks.

Methods The Mosaic + Mixup combined data augmentation approach was adopted to improve the model's adaptability to diverse scenarios and strengthen its robustness and generalization in intricate real-world settings and ambiguous conditions. In response to challenges pertaining to overlapping targets and inconspicuous features due to illumination variations, a coordinate attention (CA) mechanism was integrated into the YOLOv5 network model to amplify its feature extraction capacity. Additionally, to improve the regression accuracy of prediction borders, an Efficient Intersection over Union (EIOU) function was incorporated to calculate the width-height difference between prediction and real borders to replace the aspect ratio, thus further elevating the detection accuracy of the algorithm.

Results The YOLO-MMCE algorithm was validated using the datasets comprised of construction site photos captured by surveillance cameras in the high-consequence areas along the Lanzhou-Zhengzhou-Changsha product oil pipeline. The results revealed precision enhancements in engineering vehicle target detection under real-world conditions, resulting from the YOLOv5 algorithm improvements in three aspects. These advancements led to an overall Mean Average Precision (mAP) of 84.8%, a 6.9% increase over the original YOLOv5 algorithm. The target detection mAPs for excavators, loaders, rollers, and heavy trucks were raised by 4.4%, 7.5%, 9.5%, and 6.0% respectively.

Conclusion The YOLO-MMCE algorithm provides an efficient solution for detecting overlapped targets and engineering vehicle targets in environments with varying sunlight conditions, illustrating its values in practical applications.