# File-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
## 1 Introduction  
As urbanization progresses, the demand for human housing is increasing. High-rise buildings have become widely used architectural structures in the construction industry [1, 2, 3]. In some modern construction projects, the use of multiple cranes to accomplish challenging tasks is becoming increasingly common [4]. As important equipment in the construction of high-rise buildings at construction sites, tower cranes are widely used in various construction scenarios and provide an effective means of vertical and horizontal transportation for materials in high-rise and super high-rise buildings [5]. As a core component in the operation of tower cranes, the position and status of the hook directly affect construction safety and the accuracy of material transportation. In China, there werea total of 157 tower crane accidents from 2019 to 2022, resulting in 224 deaths [6]. The types of accidents mainly involved falling objects, crushing collisions, and electric shocks. Therefore, real-time and accurate detection and tracking of tower crane hooks are crucial for ensuring construction safety. In the traditional construction process, tower crane operators can only obtain limited information about the construction site through visual observation or restricted means such as video surveillance and intercom systems [7]. This not only reduces construction efficiency but also increases safety hazards. With the development of video surveillance technology and sensor technology, technologies such as panoramic bird's-eye view imaging [8], pose estimation [9], sensor positioning and angle detection [10], and edge extraction algorithms [11] have been applied to tower crane hook monitoring. These technologies effectively reduce the safety risks at construction sites and improve work efficiency. However, due to high equipment installation costs, limited detection scenarios, high algorithm complexity, and difficulties in deployment, the application scope of these technologies is difficult to expand. In recent years, the advancement of CNNs (Convolutional Neural Networks) has led to the emergence of deep learning-based object detection technologies as a prominent research focus for detecting tower crane hooks. [PERSON] et al. [12] introduced a bridge crane sway angle detection system based on the YOLOv3 algorithm. This system utilizes algorithms to determine the crane hook's position and calculates its swing angle by combining the hoisting rope length and hook deviation, enabling closed-loop anti-sway control. However, this approach is hampered by slow detection speed and limited angles for hook detection, resulting in incomplete hook information. [PERSON] et al. [13] presented an object detection algorithm guided by a Transformer that enhances YOLOv5. This method enhances the backbone with a Transformer structure and a BIFPN (Bidirectional Feature Pyramid Network) module into the neck structure, effectively capturing the global and semantic information of small targets. As a result, this approach significantly reduces the false detection rate and achieves precise localization of the crane hook. However, the collected dataset for crane hooks is relatively limited, and the model size has not been optimized, presenting challenges for deployment on edge devices. [PERSON] et al. [14] developed a tower crane hook fall hazard identification method based on an improved YOLOv5, combining SENet (Squeeze and Excitation Networks) attention mechanism, GIoU (Generalized Intersection over Union) loss function, and a layer for detecting small targets. This approach notably enhances detection accuracy and alleviates issues such as missed and false detections of crane hooks. Nevertheless, some frame omissions still occur in the detection process, and further improvements are required to achieve real time detection. Focusing on the problem of missed and false detections when the YOLO algorithm is used to recognize small target objects, [PERSON] et al.