"This comprehensive text/ book presents a broad review of both traditional (i.e., conventional) and the deep learning aspects of object detection in various adversarial conditions of real-world in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of objects, applications of object detection, and challenges of real-world faced by the research community for object detection task. The book addresses various degradations and artifacts of real-world for object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the pre-eminent researchers in the field, addressing not only the background of visibility enhancement, but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative challenges of real-world. Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging conditions of real world. The book will be of great interest to a broad audience ranging from researchers and practitioners, graduate and post graduate students involved in computer vision task with respect to object detection in degraded and non-degraded vision problems of real-world"--