## 1 Introduction  
Tower cranes are essential for large-scale construction projects, particularly in the development of high-rise buildings and infrastructure. However, ensuring the safety of crane operations remains a persistent challenge, as improper behaviors such as smoking or mobile phone usage are significant contributors to accidents. While behavioral recognition techniques have been explored for monitoring unsafe behaviors, their reliance on continuous spatial-temporal analysis makes them computationally expensive and impractical for real-time monitoring. Instead, object detection techniques, which focus on identifying specific objects like mobile phones and cigarettes in individual image frames, provide a more efficient alternative.  
In recent years, deep learning-based object detection models have advanced significantly, with the YOLO (You Only Look Once) series [1, 2, 3, 4, 5], PP-YOLO [6], and YOLOv8 emerging as prominent frameworks. YOLOv7 [7], for example, achieves an optimal trade-off between accuracy and speed, making it suitable for real-time applications. However, when applied to small object detection, these models exhibit critical shortcomings. Small objects occupy minimal pixel areas and lack salient features, which limits the ability of conventional feature extraction methods to distinguish them effectively. PP-YOLO, while enhancing detection through improved data augmentation and training strategies, struggles to achieve consistent precision for extremely small targets in cluttered environments. Similarly, YOLOv8 introduces a more efficient network architecture and anchor-free strategies, yet its performance remains constrained by challenges such as semantic disparity across scales and interference from complex backgrounds. Faster R-CNN [8], despite its accuracy, suffers from high computational overhead, making it impractical for real-time applications. These limitations manifest prominently in scenarios like tower crane operator surveillance, where objects such as cigarettes and mobile phones are often obscured by intricate backgrounds, variable lighting, and visual noise. The inability of these models to achieve robust performance in detecting small objects under such challenging conditions highlights the need for further architectural enhancements.  
To address these issues, we propose YOLO-TC, an enhanced version of YOLOv7 specifically optimized for small object detection in complex environments. The primary contributions of this work are as follows:  
1. We introduce ECA-CBAM, a novel channel-spatial attention module that integrates the strengths of Efficient Channel Attention (ECA) [9] and the Convolutional Block Attention Module (CBAM) [10]. By utilizing lightweight 1D convolution for cross-channel interactions, ECA-CBAM improves feature extraction efficiency while avoiding information loss.
2. To address multi-scale detection challenges, we propose the Hierarchical Asymptotic Path Aggregation Network (HA-PANet), which mitigates the semantic gap between non-adjacent feature maps. This enhancement enables more effective multi-scale feature fusion and improves the localization accuracy for small objects.
3. We conduct extensive experiments on a custom tower crane dataset and the PASCAL VOC benchmark. Comparative analyses with state-of-the-art models, including Retina-Net [11], YOLOv5, YOLOX, YOLOv7 and YOLOv8, demonstrate that YOLO-TC achieves superior performance in detecting small objects while maintaining competitive inference speed and computational efficiency.  
The experimental results confirm that YOLO-TC effectively addresses the limitations of existing methods, offering significant improvements in accuracy, robustness, and generalizability for small object detection tasks. By balancing precision and real-time applicability, YOLO-TC provides a reliable solution for safety-critical scenarios, such as monitoring tower crane operations, where the accurate detection of small objects is paramount.