# Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
## 2 A Dynamic Path Planning Model for Tower Crane Operations
### Path Safety Risk Evaluation Module  
To estimate the risk of falling objects during tower crane operations, it is necessary to define the potential drop zones of the operations [29]. The Occupational Safety and Health Administration (OSHA) in the United States has established health and safety requirements for construction cranes in its Federal Regulations (CFR) [30]. CFR 1926.1425 defines the drop zone as \"an area where materials that are reasonably foreseeable to be partially or fully suspended during an accident may fall (including, but not limited to, the area directly beneath the load being lifted)\". Based on the CFR's definition, in this research, it is assumed that the dropped load follows a parabolic trajectory, as shown in Figure 2, and the operational hazard radius can be set around the tower crane hook to define the potential drop zone. In construction projects, workers, critical machinery, and important work areas are significant protected objects. This study focuses on mobile workers rather than static objects. Figure 1: Dynamic path planning model framework for tower crane operations. Figure 2: Potential drop zone of the load. CNN, R-CNN, Mask R-CNN, and other models used in deep learning have shown significant effects in object detection, image segmentation, trajectory tracking, and other fields. However, two-stage region-based neural networks such as R-CNN have high computational costs in image recognition and classification. To achieve real-time operation, a cutting-edge object detector called YOLO emerged in 2016, employing a single-stage detection method that directly predicts bounding boxes and classes within a grid on the image, significantly speeding up the detection process compared to existing frameworks [31]. Therefore, YOLO has been widely applied and continually updated [32; 33]. [PERSON] et al. [34] used YOLOv5 for automatic identification of reflective vests, achieving an average accuracy of over 80%, meeting practical needs. [PERSON] [35] examined real-time localization and classification models for construction site debris using various deep learning models, finding that the YOLOv7 model demonstrated stronger inference speed and accuracy. To further calculate the distance between cranes and workers, mere object detection is insufficient; instance segmentation is needed to provide accurate pixel-level segmentation for each object instance in the image. YOLOv8 leads in accuracy and precision over YOLOv7, with YOLOv8-seg featuring instance segmentation capabilities [36; 37; 38]. This study employs YOLOv8-seg as an advanced instance segmentation algorithm, which offers higher accuracy and faster speed compared to previous YOLO versions (YOLOv5, YOLOv6, YOLOv7). The YOLO model, once trained, can perform instance segmentation in tower crane operation videos, outputting classes, shapes, coordinates, and confidence scores. The YOLOv8-seg model mainly consists of three modules: the input module, the backbone network module, and the task head module, as illustrated in Figure 3.
1. Input Module: This module is used for preprocessing images to ensure they can be correctly handled by the model. Generally, the main tasks of the input module include resizing, normalizing, and padding the images. All YOLO algorithms require input images to be converted to a fixed size before being fed into the detection model for training. The standardized image size designed for this study is 640 \(\times\) 640 \(\times\) 3. 2. Backbone Module: This module is built based on CNN. The convolutional layers (Conv) are used to extract and process features from the input feature map, capturing richer semantic information. CSP Bottleneck with 2 Convolutions (C2f) consists of 1 \(\times\) 1 convolution layers and multiple Bottleneck layers, focusing on processing and merging features to extract more detailed feature information.