Record Details

Enhancement and Detection of Objects in Underwater Images using Image Super-resolution and Effective Object Detection Model

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Enhancement and Detection of Objects in Underwater Images using Image Super-resolution and Effective Object Detection Model
 
Creator Arun, R Arumuga
Umamaheswari, S
Nafesha, B
Arvindan, V Makesh
Kumar, Vengam Udaya
 
Subject CNN
Computer vision
Deep learning
Image enhancement
Object detection
Underwater objects
 
Description 1050-1060
It is imperative to build an automatic underwater object recognition system in place to reduce the costs of underwater
inspections as well as the associated risks. An effective method of detecting underwater objects from underwater images of
aquatic after enhancing them using the Image Super-resolution technique is proposed in this study. The proposed approach
comprises of two major sections, Underwater Image Enhancement, and Object detection. To enhance the underwater
images, a lightweight Reduced Cascading Residual Network (RCARN) is proposed that imposes the Image Super-resolution
technique. Later, the enhanced images generated by the RCARN model are supplied for the object detection process, where
a significant object detection model, YOLOv3 is employed in this study. To improve its performance, this YOLOv3 is
trained on one of the largest datasets, the COCO data, followed by being fine-tuned using enhanced Underwater images. The
dataset utilized in this work contains 6 classes of underwater objects namely dolphin, jellyfish, octopus, seahorse, starfish,
and turtle. All these images are actual real field images collected from various sources. With this proposed approach, a better
overall ACS and mAP of 95.44% and 75.33% are achieved here, which are improved by ~8.75% and ~15%, respectively
when compared to actual collected low-resolution images.
 
Date 2022-10-28T09:42:52Z
2022-10-28T09:42:52Z
2022-10
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/60745
https://doi.org/10.56042/jsir.v81i10.61397
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(10) [Oct 2022]