Vehicle crash detection plays a crucial role in ensuring the safety of drivers, passengers, and pedestrians on roads. With the advancements in computer vision and deep learning algorithms, object detection techniques have become popular for identifying and localizing objects of interest in images and videos. One such algorithm, Yolov7, has gained significant attention due to its accuracy and efficiency in real-time object detection tasks. In this paper, we propose a vehicle crash detection system using Yolov7, which aims to detect and classify crashed vehicles in real-time scenarios. The proposed system follows a multi-step approach, starting with the collection and annotation of a diverse dataset containing both normal driving scenarios and instances of vehicle crashes. The dataset is annotated by labelling the bounding boxes around the vehicles and classifying them as normal or crashed. This annotated dataset is then used for training the Yolov7 model. Training the Yolov7 model involves feeding the input images or frames through the network and optimizing the model parameters using gradient descent. The model learns to detect and classify vehicles, distinguishing between normal and crashed instances. The training process requires a powerful GPU and can be computationally intensive, depending on the size of the dataset and complexity of the model. Once the Yolov7 model is trained, it is evaluated on a separate validation or test set to assess its performance. The evaluation results provide insights into the model’s effectiveness and help identify areas for improvement. To deploy the vehicle crash detection system in real-world scenarios, the trained model is applied to new, unseen images or videos. The Yolov7 model detects and localizes vehicles in the input data, and the classification labels are analyzed to determine if any of the detected vehicles are involved in a crash. Although the proposed system relies on Yolov7 for object detection, it is important to note that crash detection is a complex task that involves more than just the object detection algorithm. In conclusion, this paper presents a vehicle crash detection system using Yolov7, which demonstrates the potential of deep learning algorithms for real-time crash detection. The proposed system leverages the power of Yolov7 to accurately detect and classify crashed vehicles, contributing to improved road safety.