MFLD-net: a lightweight deep learning network for fish morphometry using landmark detection
Saleh, Alzayat, Jones, David, Jerry, Dean, and Azghadi, Mostafa Rahimi (2023) MFLD-net: a lightweight deep learning network for fish morphometry using landmark detection. Aquatic Ecology, 57. pp. 913-931.
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Abstract
Monitoring the morphological traits of farmed fish is pivotal in understanding growth, estimating yield, artificial breeding, and population-based investigations. Currently, morphology measurements mostly happen manually and sometimes in conjunction with individual fish imaging, which is a time-consuming and expensive procedure. In addition, extracting useful information such as fish yield and detecting small variations due to growth or deformities, require extra offline processing of the manually collected images and data. Deep learning (DL) and specifically convolutional neural networks (CNNs) have previously demonstrated great promise in estimating fish features such as weight and length from images. However, their use for extracting fish morphological traits through detecting fish keypoints (landmarks) has not been fully explored. In this paper, we developed a novel DL architecture that we call Mobile Fish Landmark Detection network (MFLD-net). We show that MFLD-net can achieve keypoint detection accuracies on par or even better than some of the state-of-the-art CNNs on a fish image dataset. MFLD-net uses convolution operations based on Vision Transformers (i.e. patch embeddings, multi-layer perceptrons). We show that MFLD-net can achieve competitive or better results in low data regimes while being lightweight and therefore suitable for embedded and mobile devices. We also provide quantitative and qualitative results that demonstrate its generalisation capabilities. These features make MFLD-net suitable for future deployment in fish farms and fish harvesting plants.
| Item ID: | 79539 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1573-5125 |
| Keywords: | Automated phenotyping, Computer vision, Convolutional neural networks image and video processing, Deep learning, Fish morphology, Machine learning |
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| Copyright Information: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Date Deposited: | 27 Jul 2023 01:59 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 90% 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3003 Animal production > 300301 Animal growth and development @ 10% |
| SEO Codes: | 10 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 1002 Fisheries - aquaculture > 100299 Fisheries - aquaculture not elsewhere classified @ 100% |
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