Learn about Object Detection Object detection is a fundamental task in the field of computer vision, with widespread applications in areas such as industrial automation, security, image recognition, and autonomous vehicles. It involves identifying and locating specific objects in images or videos, playing a crucial role in the interpretation of visual environments by computer systems. For a comprehensive literature review on object detection, an essential work to consider is the seminal paper by Joseph Redmon and Santosh Divvala, titled "You Only Look Once: Unified, Real-Time Object Detection" (2016). This work introduced the groundbreaking YOLO (You Only Look Once) algorithm, which revolutionized object detection by enabling real-time identification with a single pass through the neural network. Since then, YOLO has served as a reference for various approaches in the field. Another significant contribution is the paper by Tsung-Yi Lin et al., "Focal Loss for Dense Object Detection" (2018). This work introduces the concept of focal loss, which enhances object detection in challenging situations, such as the presence of small objects or complex backgrounds. Focal loss has become an essential component in many state-of-the-art object detection models. For a more comprehensive review, the book "Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani (2019) provides a detailed analysis of deep learningbased object detection techniques. It covers everything from the fundamentals to advanced strategies, offering a complete overview of the current landscape of object detection. These bibliographic references provide a solid foundation for understanding the advancements and challenges in the field of object detection, enabling a critical and comprehensive review of the state of the art and emerging trends in this exciting discipline of computer vision.
