Object detection in low-visibility environments is a critical challenge, particularly for applications like autonomous vehicles and safety monitoring systems. In this work, we explore advanced detection techniques under adverse conditions, leveraging the YOLO11n.pt model for its high performance and real-time capabilities. A com- prehensive review of related works highlights significant progress in the field, such as the use of Visibility Context for robust 3D recognition and thermal imaging for im- proved accuracy during adverse weather. However, these methods often face limi- tations in terms of computational complexity, sensitivity to environmental factors, or reliance on specific hardware. By adopting YOLO11n.pt, we aim to overcome these challenges, providing a solution that maintains high precision and adaptability in dynamic and low-visibility settings. Preliminary results demonstrate the model’s potential in detecting objects accurately even under rain, fog, and poor lighting con- ditions, paving the way for safer and more efficient object recognition systems.