## 1 Introduction
[19] proposed a two-stream deep learning network called Attention R-CNN to tackle the inability of traditional object detection methods to recognize not just the presence of objects but also their characteristic properties, such as whether they are safe, dangerous, or crashed, present in autonomous driving systems. The first stream leverages a modified Faster R-CNN to detect object bounding boxes and their classes, while the second stream, employs an attention mechanism to integrate global scene context with local object features, enabling the recognition of properties like \"crashed\" or \"dangerous\". [PERSON] et al. [20] address the shortcomings of existing accident detection systems, such as high false-positive rates, vehicle-specific compatibility issues, and high implementation costs, which delay emergency responses and increase road fatalities in smart cities. They developed a low-cost IoT kit equipped with sensors to collect real-time data like speed, gravitational force, and sound, which is transmitted to the cloud. A deep learning model based on YOLOv4, fine-tuned with ensemble transfer learning and dynamic weights, processes this data alongside video footage from a Pi camera to verify accidents and minimize false detections. [PERSON] et al. [21] proposed a convolutional neural network (CNN)-based model for car crash detection using dashcam images, aiming to enable rapid and precise accident identification. [PERSON] et al. [22] proposed a framework that integrates IoT sensors with machine learning to enable real-time accident detection and risk prediction. IoT sensors installed on vehicles continuously collect data such as speed, acceleration, brake force, and lane deviation, transmitting this information wirelessly to a central system. The data are then processed using the Adaptive Random Forest algorithm that dynamically learns from incoming data to identify patterns indicative of accidents or high-risk situations. When parameters exceed predefined thresholds, such as excessive speed or sudden braking, the system issues alerts to drivers and may activate safety mechanisms like automatic braking. Pre-trained models have shown promising results in transfer learning scenarios, adapting to specialized tasks with limited data [23,24]. However, their application to car collision detection using dashcam images remains underexplored, particularly in optimizing hyperparameter configurations for real-world deployment. This study provides a systematic evaluation of SOTA deep learning architectures for car collision detection to test their adaptability and performance under different hyper parameter settings. We fine-tune seven pre-trained models (Table 1) under varied hyperparameter configurations (Table 2) to identify optimal trade-offs between accuracy, computational efficiency, and localization reliability. The proposed framework holds significant practical implications. By automating collision detection, municipalities can reduce reliance on error-prone manual monitoring, accelerate emergency response times, and dynamically optimize traffic signals to mitigate congestion. The rest of this paper is organized as follows: The Section 2 outlines the processes of data collection, data labeling, model selection, model customization, loss functions, optimizers, schedulers, and the training pipeline. The Section 3 explains the benchmark metrics for each model, and the Section 4 presents comparative findings of all the best models selected for each performance metric. The Section 5 explores the effects of different parameters in detail, while the Conclusion provides a summary and final remarks.