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Deep learning techniques for marine object detection in challenging underwater environments
Underwater environments present unique detection challenges: reduced brightness, haziness, colour variation, water turbidity, dynamic currents, camouflaged organisms, occlusion, and extreme size variation. Underwater Marine Object Detection Using Deep Learning addresses these obstacles by surveying recently developed AI techniques for marine object detection and classification. Authored by Ashish Kumar and Shikha Bhalla, this reference consolidates detection pipelines, public datasets, and performance benchmarks into a single authoritative volume.
The book provides exhaustive analysis of existing marine object datasets evaluated against real-time detection challenges. Performance metrics used across published works are systematically reviewed for direct comparison. Coverage extends to detect and analyse the life below water that aligns with UN Sustainable Development Goal 14 for marine protection. Applications span ecological monitoring, underwater robotics, pipeline detection, underwater archaeology, and defence operations.
Readers will also find:
Designed for marine scientists, AI researchers, PhD and postgraduate students, and professionals in underwater object detection, this book serves as a consolidated reference for building robust detection and classification models. Defence and industry R&D units operating AUV and ROV platforms will find the systematic coverage of detection pipelines directly applicable to operational requirements.
Deep learning techniques for marine object detection in challenging underwater environments
Underwater environments present unique detection challenges: reduced brightness, haziness, colour variation, water turbidity, dynamic currents, camouflaged organisms, occlusion, and extreme size variation. Underwater Marine Object Detection Using Deep Learning addresses these obstacles by surveying recently developed AI techniques for marine object detection and classification. Authored by Ashish Kumar and Shikha Bhalla, this reference consolidates detection pipelines, public datasets, and performance benchmarks into a single authoritative volume.
The book provides exhaustive analysis of existing marine object datasets evaluated against real-time detection challenges. Performance metrics used across published works are systematically reviewed for direct comparison. Coverage extends to detect and analyse the life below water that aligns with UN Sustainable Development Goal 14 for marine protection. Applications span ecological monitoring, underwater robotics, pipeline detection, underwater archaeology, and defence operations.
Readers will also find:
Designed for marine scientists, AI researchers, PhD and postgraduate students, and professionals in underwater object detection, this book serves as a consolidated reference for building robust detection and classification models. Defence and industry R&D units operating AUV and ROV platforms will find the systematic coverage of detection pipelines directly applicable to operational requirements.
Atsiliepimai