## 2 Related Work
ResNet, short for deep Residual Networks, is renowned as one of the most advanced and effective CNNs for image recognition [36]. In 2015, ResNet made a significant breakthrough by achieving victory in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC-15) with an impressive top 5 error rate of 3.57% [32]. For example, ResNet-50, employing residual learning, is a pioneering architecture in deep CNNs [32]. With a total of 50 layers, it has demonstrated exceptional precision in computer vision. The ResNet-50 network comprises five convolutional layers, each contributing to its unparalleled performance in image classification and recognition tasks. CNNs are frequently employed to classify medical images with high accuracy and superior performance. CNNs analyze input images and generate an output to ascertain the presence or absence of a disease. Also, CNNs have made their way into the construction safety industry, enabling the analysis and classification of construction safety documents to uncover hidden dangers quickly. This facilitates the implementation of corresponding measures to prevent similar accidents in the future. While there has been relatively limited research on the application of CNNs to detect unsafe events in construction site images, the further exploration of computer vision technologies in construction and transportation safety studies is needed. CNNs are extensively utilized for their highly accurate and superior performance in classifying medical images. They analyze input images to determine the presence or absence of diseases (e.g., [29; 37]). Moreover, CNNs have been utilized in the construction safety industry, enabling the swift analysis and classification of construction safety documents to identify hidden dangers promptly [10; 14]. This facilitates the implementation of preventive measures to avoid similar accidents in the future. Despite relatively limited research on CNNs' application in detecting unsafe events in construction site images, there is a pressing need for the further exploration of computer vision technologies in construction and transportation safety studies. Despite significant advancements in applying AI and DL techniques for image classification, several research gaps persist in pedestrian safety analysis using crash diagrams. First, existing studies predominantly focus on crash narratives for severity prediction, with no prior research utilizing crash diagrams as a structured source of safety data. As a result, pedestrian crash diagrams remain an untapped resource, and their potential for automated feature extraction has not been explored. Second, while CNN architectures have demonstrated strong performance in image recognition tasks, their application in traffic safety, particularly for classifying pedestrian crash features, remains unexamined. Comparative evaluations of different CNN architectures for crash diagram classification are nonexistent, leaving a gap in understanding their relative effectiveness. Third, integrating automated feature extraction with real-world safety applications is still in its early stages. There is a need for methodologies that enhance data completeness and support real-time crash analysis and decision-making. This study addresses these gaps by being the first to leverage CNN models to classify pedestrian crash diagrams, improving data reliability and contributing to more effective pedestrian safety interventions.