Multi-level Approach for Detection and Identification of Camouflaged Lifeform: A comprehensive detailing of identifying camouflaged Lifeforms
Year: 2024
Type: conference-paper
Abstract: Camouflaged object detection and Identification (CODnI) in natural environment, especially involving lifeform, pose inherent challenges due to computational complexity. Leveraging the insights from human vision, a multilevel CODnI approach has been presented to improve both the detection accuracy and the computational efficiency, addressing the challenges of high miss rates, high type-1 and type-II errors and low confidence. Further, the proposed machine vision approach has been augmented with a physics-based feature using infrared (IR) band in addition to red, green and blue (RGB) data. Performance of the model has been evaluated for a camouflaged natural environment image dataset that have been used extensively over last few years. Presented technique found to achieve significantly high detection rate (miss rate 2.63% only), F1 score of 0.97 and 0.95 confidence level (p value 0.05).
Institution: Amrita Vishwa Vidyapeetham
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4403854091