# File-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
## 2 Techniques and Approaches
### Tower Crane Safety Status Monitoring System  
Tower cranes primarily consist of foundational structures, lifting mechanisms, slewing systems, and support systems. Enhancing the characteristics of the crane itself and collecting and early warning of operational parameters are crucial aspects of tower crane safety monitoring systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Advantage** & **Weakness** & **Applicable Scene** & **FPS** \\ \hline R-CNN [25] & AlexNet & Combine CNN with & The detection speed is slow, & & 0.03 \\ VGG16 & candidate box method & time-consuming, and the fixed & Object detection & 0.5 \\  & & & image input size is fixed & & \\ \hline Fast R-CNN [26] & VGG16 & Use POI Pooling to extract features and save time & The calculation of candidate & & \\  & & features and save time & region selection method & Object detection & 7 \\ \hline Faster R-CNN [27] & VGG16 & Replace the regional & & & \\  & & suggestion with RPN to & & & \\  & & speed up the training and & & & \\  & & improve the accuracy & & & \\ \hline YOLO [28] & Darknet & & The detection accuracy of small & & \\  & & & targets is low, and it is difficult & & \\  & & & to deal with complex & & \\ YOLO [28] & Darknet & simple and efficient, and & & backgrounds. The initial model & & \\  & & & widely used & has high complexity and is & & \\  & & & difficult to be applied to devices & & \\  & & & with limited resources & & \\ \hline SSD [30] & VGG16 & Multi-scale anchor box & The model is difficult to & Multi-scale object & & \\  & & detection, efficiency & & coverage and the detection & & \\  & & & accuracy of the model is low & & video analysis & \\ \hline RetinaNet [31] & ResNet & The problem of category & Sample imbalance is caused & & \\  & & imbalance is solved through & during intensive & & \\  & & Focal Loss & sample training & & \\ \hline Corner Net [35] & Hourglass & Without anchor point, the & The internal features are & & \\  & \(-52/104\) & & detection is changed to corner & missing, and the corner feature & & \\  & & point detection, and the & points of the same object need & & \\  & & positioning performance & to be classified, which leads to & & \\  & & is good & high computational complexity & & \\ \hline  & & & & & \\ \hline CenterNet [33] & Hourglass & No anchor point, the & & post-processing, the center & & \\  & \(-52/104\) & & detection is changed to a & point positioning of small & & \\  & & triplet, and the reasoning & targets is not accurate enough, & & \\  & & speed is fast & and the ability to deal with & & \\  & & complex scenes is limited & & & \\ \hline Cascade R-CNN [34] & ResNet + FPN & The cascade structure & The computing overhead & & \\  & & improves the detection effect & is large & & \\ \hline Efficient-Det & EfficientNet & & with composite scaling and & & \\  & & BiFPN network, excellent & The model is complex, the & & Mobile devices, \\  & D1\(\sim\)D7 [32] & EfficientNet & & multi-scale target & training time is long & embedded devices & \\  & & detection performance & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of object detection algorithm. To address the issue of predicting the pitching angle response in a narrow range during the operation of dual tower cranes, [PERSON] et al. [36] effectively determined the lower and upper bounds of the interval amplitude angle response vector for dual crane systems by modeling the uncertain interval structural parameters with dynamic. This approach successfully mitigated collision risks in dual tower crane systems. [PERSON] et al. [37] designed an adaptive integral sliding mode control method for payload anti-sways in tower cranes affected by external disturbances, achieving continuous, jitter-free control of crane operations. [PERSON] et al. [38] introduced an approach for detecting and measuring structural surface cracks based on unmanned aerial vehicle (UAV) images, addressing the challenge of inaccessible areas for crack detection in large crane structures.