MAD-YOLO: A quantitative detection algorithm for dense small-scale marine benthos
Institution:
1. Laboratório de Parasitologia Médica e Biologia de Vetores, Faculdade de Medicina, Universidade de Brasília, Brasília, DF, Brazil;2. Programa de Pós-Graduação em Zoologia, Departamento de Zoologia, Universidade de Brasília, Brasília, DF, Brazil;3. Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523, USA;4. Biodiversity Institute, University of Kansas, Lawrence, KS, USA;1. Department of Computer Science and Engineering, Y.S.R University College of Engineering & Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India;2. Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Guntur, Andhra Pradesh, India;3. Department of Computer Science and Engineering, V.R. Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India;1. Shijiazhuang Institute of Railway Technology, Shijiazhuang 050018, China;2. School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;3. College of Forestry, Beijing Forestry University, Beijing 100083, China;4. Pingwu Panda Valley Family Farm, Pingwu 622550, China;5. The Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;6. UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia;1. Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India;2. Department of Health and Family Welfare, Government of Punjab, India
Abstract:
Marine biological resources are abundant, and the reasonable development, research and protection of marine biological resources are of great significance to marine ecological health and economic development. At present, underwater object quantitative detection plays a very important role in marine biological science research, marine species richness survey, and rare species conservation. However, the problems of a large amount of noise in the underwater environment, small object scale, dense biological distribution, and occlusion all increase the detection difficulty. In this paper, a detection algorithm MAD-YOLO (Multiscale Feature Extraction and Attention Feature Fusion Reinforced YOLO for Marine Benthos Detection) is proposed, which is based on improved YOLOv5 is proposed to solve the above problems. To improve the adaptability of the network to the underwater environment, VOVDarkNet is designed as the feature extraction backbone. It uses the intermediate features with different receptive fields to reinforce the ability to extract feature. AFC-PAN is proposed as the feature fusion network so that the network can learn correct feature information and location information of objects at various scales, improving the network's ability to perceive small objects. Label assignment strategy SimOTA and decoupled head are introduced to help the model better handles occlusion and dense distribution problems. Experiments show the MAD-YOLO algorithm increases mAP0.5:0.95 on the URPC2020 dataset from 49.8% to 53.4% compared to the original YOLOv5. Moreover, the advantages of the model are visualized and analyzed by the method of controlling variables in the experimental part. The experiments show that MAD-YOLO is suitable for detecting blurred, dense, and small-scale objects. The model performs well in marine benthos detection tasks and can effectively promote marine life science research and marine engineering implementation. The source code is publicly available at https://github.com/JoeNan1/MAD-YOLO.
