<p>This study compares three deep learning algorithms—Convolutional Neural Networks (CNN), You Only Look Once (YOLO), and Faster R-CNN—for vehicle damage detection and classification, focusing on real-world deployment challenges. It evaluates their performance under varying lighting and environmental conditions to determine their suitability for practical applications like insurance claims processing and fleet management. The findings indicate that the CNN model achieved an accuracy of 92%, while YOLO, known for its speed and efficiency, exhibited a slightly lower accuracy at 90% with higher precision and recall. Faster R-CNN, demonstrated the highest accuracy at 94%, excelling in precise object localization. The findings highlight that Faster R-CNN performed best under challenging conditions, while YOLO was more efficient in real-time applications due to its faster processing time. Furthermore, this study identifies significant integration challenges for resource-intensive models like Faster R-CNN and emphasizes the importance of explainability techniques such as saliency maps and Grad-CAM, in enhancing model transparency and stakeholder trust. The results underscore the necessity of selecting deep learning models based on the specific deployment requirements, balancing accuracy, processing speed, and explainability. Finally, the study’s findings have significant implications for industries such as insurance and fleet management, where accurate and explainable damage detection is vital. It suggests that future work should focus on improving model robustness, integration ease, and explainability to enhance real-world practice.</p>