Solving the night object detection problem based on the PyTorch framework and YOLOv5architecture

Object recognition in difficult night conditions is a complex task, the solution of which is necessary to create effective video surveillance systems, automotive technologies and robotics. The article discusses an approach to solving this problem based on the use of the Pitch framework and the YOLOv5 architecture. An overview of current methods provided, and the stages of data preparation, model setup, and learning optimization described. Experimental results demonstrating the effectiveness of the approach in low-light conditions presented. The materials of the article can be useful to developers, researchers and specialists in the field of computer vision to improve the accuracy of object detection in difficult conditions.