## 1 Introduction  
Detection information for construction equipment focuses on capturing and interpreting data related to the movement and operation of machinery, including cranes, loaders, and trucks. By continuously monitoring these details, construction sites can enhance safety awareness and minimize collisions through real-time alerts and preventive measures. As a result, optimized detection methods help create safer, more efficient, and increasingly automated construction environments [1].  
A promising solution for construction equipment detection on construction sites is incorporating object detection using deep learning techniques applied to images captured from cameras, such as surveillance cameras and body-worn cameras. Object detection involves localizing and classifying target objects within images using bounding boxes [2]. In many fields, including construction, the most commonly used and well-established object detection methods are based on You Only Look Once (YOLO) [3, 4, 5]. YOLO has undergone continuous development, resulting in several versions with significant improvements.  
In this research, YOLOv10 was selected for its advanced capabilities in accuracy and flexibility. Compared to earlier versions, YOLOv10 offers significant enhancements in feature extraction and robustness to varying object sizes and occlusions. Its modular design allows for extensive customization, with the backbone architecture being one of its most critical components. The backbone is responsible for feature extraction, and it is often interchangeable, directly affecting both detection accuracy and computational efficiency [6].  
Traditionally, CNN-based backbone architectures are popular due to their reliable accuracy. Meanwhile, transformer-based architectures have emerged for enhanced object detection performance in many fields [7; 8]. To evaluate whether current methods meet the requirements for high-accuracy and real-time computation in heavy equipment detection, this research explored multiple object detection methods based on YOLOv10, with various backbone architectures specifically designed using images from both surveillance and body-worn cameras. The main contributions of this paper can be summarized as follows:  
1. Two different types of camera data (surveillance and body-worn cameras) were used to assess the robustness and generalizability of model performance.
2. A YOLOv10-based approach was introduced for equipment detection on construction sites. Advanced transformer-based backbone architectures were implemented.
3. A comprehensive comparison of object detection methods was conducted, contrasting traditional, reliable architectures with advanced transformer-based architectures.  
This paper is organized as follows. In Section 2, research related to object detection for heavy equipment and the YOLO series is reviewed to identify the research gap. In Section 3, an overview of the proposed approach is presented, together with detailed procedures for creating it. Section 4 describes the experiments for demonstrating the proposed approach. In Section 5, an analysis of the experimental results is provided, along with conclusions and suggestions for future work.