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

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Title Statement <p>Enhancement and Detection of Objects in Underwater Images using Image Super-resolution and Effective Object Detection Model</p>
 
Added Entry - Uncontrolled Name Arumuga Arun, R ; Anna University-MIT Campus, Chennai 600 044, Tamil Nadu, India
Umamaheswari, S ; Anna University-MIT Campus, Chennai 600 044, Tamil Nadu, India
Nafesha, B ; Anna University-MIT Campus, Chennai 600 044, Tamil Nadu, India
Makesh Arvindan, V ; Anna University-MIT Campus, Chennai 600 044, Tamil Nadu, India
Udaya Kumar, Vengam ; Anna University-MIT Campus, Chennai 600 044, Tamil Nadu, India
 
Uncontrolled Index Term CNN, Computer vision, Deep learning, Image enhancement, Object detection, Underwater objects
 
Summary, etc. <p>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.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-11-13 06:46:16
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/61397
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 81, ##issue.no## 10 (2022): Journal of Scientific & Industrial Research
 
Language Note en