Automatic coral reef fish identification and 3D measurement in the wild
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Abstract (arXiv)
In this paper we present a pipeline using stereo images in order to automatically identify, track in 3D fish, and measure fish population.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 12 Dec 2023.
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19 of 43 references.
References · 19
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2017Mask R-CNNcited by 7,259
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2015Fast R-CNNcited by 2,930
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
- 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
- 2016Simple Online and Realtime Trackingcited by 862
- 2021Unbiased Teacher for Semi-Supervised Object Detectioncited by 305
7 more
- 2020A Simple Semi-Supervised Learning Framework for Object Detectioncited by 282
- 2021TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Trackingcited by 93
- 2018Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Datasetcited by 90
- 2020A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysiscited by 57
- 2021Puzzle-CAM: Improved localization via matching partial and full featurescited by 37
- 2015Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videoscited by 4
- 2020Analysis of the Scalability of a Deep-Learning Network for Steganography "Into the Wild"cited by 3
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