The field of computer vision is witnessing significant advancements in object detection and scene understanding, driven by the development of large-scale datasets and innovative deep learning approaches. Researchers are exploring the application of these technologies in various domains, including autonomous driving, surveillance, and inspection. A key trend is the use of synthetic data to augment real-world datasets, improving the diversity and robustness of models. Another area of focus is the evaluation of object detection models under varying conditions, such as different image resolutions and environmental factors. Noteworthy papers include:

• YOLO for Knowledge Extraction from Vehicle Images, which achieved high accuracy in vehicle attribute identification using a multi-view inference approach.
• DriveIndia, a large-scale object detection dataset for diverse Indian traffic scenes, offering a comprehensive benchmark for real-world autonomous driving challenges.
• Wind Turbine Feature Detection Using Deep Learning and Synthetic Data, which proposed a method for generating synthetic training data to detect wind turbine features with high accuracy.