1. MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model
- Author
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W.E.I. Lian-suo, Huang Shen-hao, and Ma Long-yu
- Subjects
YOLOv5 ,Marine target detection ,Feature enhancement ,Multi scale perceptual field ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances, complex target clustering, and noise from waterborne particulates.To address these issues, we propose MTD-YOLOv5.Initially, we enhance image contrast with grayscale equalization and mitigate color shift issues through color space transformation.We then introduce a novel feature extraction module, PCBR, combining max pooling and convolution layers for more effective target feature extraction from the background.Furthermore, we present the Multi-Scale Perceptual Hybrid Pooling (MHP) module.This module integrates horizontal and vertical receptive fields to establish long-range dependencies, thereby capturing hidden target information in deep network feature maps. In the Labeled Fishes in the Wild test datasets, MTD-YOLOv5 achieves a precision of 88.1% and a mean Average Precision (mAP[0.5:.95]) of 49.6%.These results represent improvements of 2.6% in precision and 0.4% in mAP over the original YOLOv5.
- Published
- 2024
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