This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions 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 real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the 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 preeminent 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 real-world challenges.

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 real-world conditions.

Topics and Features:

• Offers the first truly comprehensive presentation of aspects of the object detection in degraded and nondegraded environment.

• Includes in-depth discussion of various degradation and artifacts, and impact of those artifacts in the real world on solving the object detection problems.

• Gives detailed visual examples of applications of object detection in the real world.

• Presents a detailed description of popular imaging modalities for object detection adopted by researchers.

• Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks.

• Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures.

• Discusses techniques for detection of objects in real-world applications.

• Contains various hands-on practical examples and a tutorial for solving object detection problems using Python.

• Motivates readers to build vision-based systems for solving object detection problems in degraded and nondegraded real-world challenges.

The book will be of great interest to a broad audience ranging from researchers and practitioners to graduate and postgraduate students involved in computer vision tasks with respect to object detection in degraded and nondegraded real-world vision problems.