The field of computer vision and machine learning is rapidly advancing, with a focus on improving object detection and analysis in various applications. Recent developments have led to the creation of more accurate and efficient models for detecting and analyzing objects, including food items and wildlife products. The use of deep learning architectures such as YOLO has become increasingly popular, with modifications and improvements being made to enhance their performance. Additionally, the development of comprehensive datasets for specific domains, such as Indian cuisine, is facilitating more accurate food recognition and analysis. Noteworthy papers in this area include the proposal of a tailored solution for Bangladeshi street food calorie estimation using an improved YOLOv8 model, and the development of a machine learning-based system for detecting trade in products derived from threatened species using a smartphone application. The introduction of a real-time vision-based system for badminton smash speed estimation on mobile devices is also a significant innovation, demonstrating the potential for cost-effective and user-friendly performance analytics in sports.