# File-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
## 1 Introduction
[15] proposed a small target recognition model, which can improve the detection of small objects like tower crane hooks by adding a 160 \(\times\) 160 detection layer, improving channel attention, and strengthening the characteristics of small target objects. [PERSON] et al. [16] proposed an improved RT-DETR network for the tower crane small object detection model, which effectively improves detection accuracy and speed by integrating multi-structure reparameterization, optimizing convolution modules, and training loss functions. [PERSON] et al. [17] proposed the SGD-YOLOv5 model, which utilizes spatial to deep convolution and introduces a global attention mechanism to capture global information. Decoupling the detection head operation helps overcome the problem of spatial misalignment. Due to factors such as the small size, fast movement speed, and complex construction environment of tower crane hooks [18], in order to reduce investment costs, improve the effectiveness of detecting tower crane hook in complex scenarios, and enhance detection speed while minimizing deployment difficulties, this paper proposes a faster, lighter, and more efficient object detection algorithm called FLE-YOLO. The model introduces the FasterNet [19] network, Triplet Attention [20] mechanism, Slim neck [21] module, and Dyhead [22] structure in the backbone, neck connection layer, and head of the detector, respectively, which reduces the calculation amount while maintaining the detection accuracy. The main contributions of this paper are listed below:  
1. A novel object detection model FLE-YOLO is proposed, which adopts the FasterNet lightweight backbone network and introduces the Triplet Attention module. By focusing on the triplet interaction of spatial dimensions H, W, and channel dimension C, the feature expression of key areas of the hook is strengthened. Simultaneously, Slimneck is utilized to embed VoV-GSCSP and GSConv into the neck structure for bottom-up and top-down feature fusion. Introducing Dyhead into the head module, its unified structure of spatial, scale, and task perception can comprehensively learn target features, balancing detection accuracy and lightness. 2. To validate the effectiveness of each module, heat maps are generated to assess the impact of the Triplet Attention and Dyhead attention on crane hooks. The color distribution in the FLE-YOLO heat maps is more concentrated in the hook area than that in the original algorithm. Additionally, ablation studies are conducted to evaluate the contribution of each module within the entire network using performance metrics. 3. Extensive experiments on large datasets, COCO2017 and VOC2012, demonstrate significant improvements in computational complexity, parameter count, and detection speed of the FLE-YOLO detector compared to original network. Further experiments on a collected tower crane hook dataset show that compared to the original algorithm, computational complexity is reduced to 19.4 GFLOPs, detection speed increased to 142.857 f/s, accuracy reached 97.3% (can remain the same), AP\({}_{50}\) reached 98.3% (an increase of 0.6%), and parameter count reached 7.588 M, a reduction of 3.538 M.
4. During the testing phase at the construction site, a visual detection and tracking scheme was proposed. This involved establishing a visual monitoring interface and implementing efficient identification and tracking of hooks, ultimately ensuring both the efficiency and safety of tower crane operations. The remainder of this paper is organized as listed below: Section 2 includes the object detection algorithms and tower crane safety state monitoring systems, emphasizing the necessity of this work; Section 3 describes the methodology for collecting tower crane hook datasets; Section 4 describes the basic principles of the FLE-YOLO object detection algorithm; Section 5 presents the experimental results; and Section 6 provides a summary.