EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring

Jalata, Ibsa; Chappa, Naga V S Raviteja; Truong, Thanh-Dat; Pierce, Helton,; Rainwater, Chase; Luu, Khoa

doi:10.1109/access.2022.3203736

Cited by 9 publications

(1 citation statement)

References 49 publications

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“…Object detection has been getting attention in the computer vision community because of the rapid growth of deep learning and its importance for different applications, including security, robotics, autonomous driving, [1][2][3][4][5] image deblurring 6,7 and others. The quality of data annotation and the quantity of data play an important role in building an object detection model.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised learning for fish species recognition

Alaba

Shah

Nabi³

et al. 2023

Ocean Sensing and Monitoring XV

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Fish species recognition and detection are essential for fishery industries. Accurate and robust species classification and detection play a vital role in monitoring fish activities and identifying the distribution of a specific species, which is vital to know the endangered species. It is also essential for controlling production and overall ecosystem control and management. However, the role of current artificial intelligence technologies, such as deep learning, is limited in the ocean system compared to other areas like robotics and security. The major challenge in building a deep learning network is data availability, time, and cost of annotation and labeling. In this work, we build a semi-supervised deep-learning network to recognize fish species. The model is based on a student-teacher network where the teacher network generates pseudo-labels, and the student network is trained with the generated pseud-labels and the labeled data simultaneously. The student network updates the teacher network via an exponential moving average method. The model consists of a faster R-CNN with a feature pyramid network detector. The experimental result of the model on the challenging fish dataset shows a promising result for building semi-supervised object detection models.

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