The recent advancements in the field of computer vision and deep learning have shown significant progress in various applications, particularly in safety, environmental monitoring, and autonomous systems. The integration of deep learning models with real-time data processing has led to innovative solutions for air quality forecasting, landmine detection, and autonomous robotic manipulators. Notably, the use of simulation environments for testing and enhancing deep learning models in robotics has proven effective, showcasing improvements in both model accuracy and system robustness. Additionally, the development of physics-informed neural networks for air quality prediction has bridged the gap between traditional physics-based models and data-driven approaches, offering a more accurate and computationally efficient solution. In the realm of safety, real-time weapon detection systems and enhanced safety equipment monitoring on construction sites have been advanced through optimized deep learning models. These developments not only enhance the accuracy and efficiency of detection but also contribute to broader societal safety measures. The field is also witnessing a shift towards more interpretable and robust models, particularly in autonomous driving and violence monitoring systems, where the integration of explicit rules with implicit reasoning is being explored to improve system reliability and explainability. Overall, the current research trends indicate a strong emphasis on practical applications, real-time processing, and the integration of physical principles with machine learning to create more robust and reliable systems.