{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:57:33Z","timestamp":1768456653227,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UK Department for Environment, Food and Rural Affairs (DeFRA) Fisheries Industry Science Partnership (FISP)","award":["ECM_63777"],"award-info":[{"award-number":["ECM_63777"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Crabs and lobsters are valuable crustaceans that contribute enormously to the seafood needs of the growing human population. This paper presents a comprehensive analysis of single- and multi-stage object detectors for the detection of crabs and lobsters using images captured onboard fishing boats. We investigate the speed and accuracy of multiple object detection techniques using a novel dataset, multiple backbone networks, various input sizes, and fine-tuned parameters. We extend our work to train lightweight models to accommodate the fishing boats equipped with low-power hardware systems. Firstly, we train Faster R-CNN, SSD, and YOLO with different backbones and tuning parameters. The models trained with higher input sizes resulted in lower frames per second (FPS) and vice versa. The base models were highly accurate but were compromised in computational and run-time costs. The lightweight models were adaptable to low-power hardware compared to the base models. Secondly, we improved the performance of YOLO (v3, v4, and tiny versions) using custom anchors generated by the k-means clustering approach using our novel dataset. The YOLO (v4 and it\u2019s tiny version) achieved mean average precision (mAP) of 99.2% and 95.2%, respectively. The YOLOv4-tiny trained on the custom anchor-based dataset is capable of precisely detecting crabs and lobsters onboard fishing boats at 64 frames per second (FPS) on an NVidia GeForce RTX 3070 GPU. The Results obtained identified the strengths and weaknesses of each method towards a trade-off between speed and accuracy for detecting objects in input images.<\/jats:p>","DOI":"10.3390\/computers13050119","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T03:34:01Z","timestamp":1715571241000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detection of Crabs and Lobsters Using a Benchmark Single-Stage Detector and Novel Fisheries Dataset"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5456-5567","authenticated-orcid":false,"given":"Muhammad","family":"Iftikhar","sequence":"first","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, Ceredigion, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marie","family":"Neal","sequence":"additional","affiliation":[{"name":"Ystumtec Ltd., Pant-Y-Chwarel, Ystumtuen, Aberystwyth SY23 3AF, Ceredigion, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-8435","authenticated-orcid":false,"given":"Natalie","family":"Hold","sequence":"additional","affiliation":[{"name":"School of Ocean Sciences, Bangor University, Bangor LL57 2DG, Gwynedd, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-807X","authenticated-orcid":false,"given":"Sebastian","family":"Gregory Dal To\u00e9","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, Ceredigion, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-1192","authenticated-orcid":false,"given":"Bernard","family":"Tiddeman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, Ceredigion, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"key":"ref_1","unstructured":"FAO\/DANIDA (1999). Guidelines for the Routine Collection of Capture Fishery Data, FAO. FAO Fisheries Technical Paper."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1002\/aqc.3086","article-title":"Increasing the functionalities and accuracy of fisheries electronic monitoring systems","volume":"29","author":"Gilman","year":"2019","journal-title":"Aquat. Conserv. Mar. Freshw. Ecosyst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1093\/icesjms\/fsu212","article-title":"Design and implementation of electronic monitoring in the British Columbia groundfish hook and line fishery: A retrospective view of the ingredients of success","volume":"72","author":"Stanley","year":"2015","journal-title":"ICES J. Mar. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1093\/icesjms\/fsv030","article-title":"Video Capture of Crustacean Fisheries Data as an Alternative to On-board Observers","volume":"72","author":"Hold","year":"2015","journal-title":"ICES J. Mar. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s11160-022-09727-6","article-title":"Smartphone application use in commercial wild capture fisheries","volume":"32","author":"Calderwood","year":"2022","journal-title":"Rev. Fish Biol. Fish."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Barbedo, J.G.A. (2022). A Review on the Use of Computer Vision and Artificial Intelligence for Fish Recognition, Monitoring, and Management. Fishes, 7.","DOI":"10.3390\/fishes7060335"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100061","DOI":"10.1016\/j.atech.2022.100061","article-title":"Applications of data mining and machine learning framework in aquaculture and fisheries: A review","volume":"2","author":"Gladju","year":"2022","journal-title":"Smart Agric. Technol."},{"key":"ref_8","unstructured":"(2021). Intergovernmental Oceanographic Commission of the United Nations Educational, Scientific and Cultural Organization."},{"key":"ref_9","unstructured":"Iftikhar, M., Tiddeman, B., Neal, M., Hold, N., and Neal, M. (2023, January 14\u201315). Investigating deep learning methods for identifying crabs and lobsters on fishing boats. Proceedings of the 41st Computer Graphics and Visual Computing Conference (CGVC), Aberystwyth, UK."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"To\u00e9, S.G.D., Neal, M., Hold, N., Heney, C., Turner, R., Mccoy, E., Iftikhar, M., and Tiddeman, B. (2023). Automated video-based capture of crustacean fisheries data using low-power hardware. Sensors, 23.","DOI":"10.3390\/s23187897"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"11","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_12","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). You Only Look Once: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_14","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_15","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, J., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., and Liu, Z. (2022, January 19\u201320). Mobile-former: Bridging mobilenet and transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"ref_18","first-page":"20014","article-title":"Xcit: Cross-covariance image transformers","volume":"34","author":"Nouby","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3780","DOI":"10.1093\/icesjms\/fsab227","article-title":"A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images","volume":"78","author":"Allken","year":"2021","journal-title":"ICES J. Mar. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/gdj3.114","article-title":"A real-world dataset and data simulation algorithm for automated fish species identification","volume":"8","author":"Allken","year":"2021","journal-title":"Geosci. Data J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102117","DOI":"10.1016\/j.aquaeng.2020.102117","article-title":"A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone","volume":"91","author":"Cai","year":"2020","journal-title":"Aquac. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1093\/icesjms\/fsaa158","article-title":"Deep learning methods applied to electronic monitoring data: Automated catch event detection for longline fishing","volume":"78","author":"Qiao","year":"2021","journal-title":"ICES J. Mar. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1093\/icesjms\/fsz025","article-title":"Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system","volume":"77","author":"Salman","year":"2020","journal-title":"ICES J. Mar. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1093\/icesjms\/fsaa076","article-title":"Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks","volume":"77","author":"Tseng","year":"2020","journal-title":"ICES J. Mar. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105339","DOI":"10.1016\/j.compag.2020.105339","article-title":"Real-time robust detector for underwater live crabs based on deep learning","volume":"172","author":"Cao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108318","DOI":"10.1016\/j.compag.2023.108318","article-title":"Chinese Mitten Crab Detection and Gender Classification Method Based on Gmnet-Yolov4","volume":"214","author":"Chen","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107522","DOI":"10.1016\/j.compag.2022.107522","article-title":"Real-time detection of underwater river crab based on multi-scale pyramid fusion image enhancement and MobileCenterNet model","volume":"204","author":"Ji","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1093542","DOI":"10.3389\/fmars.2023.1093542","article-title":"A Part-based Deep Learning Network for identifying individual crabs using abdomen images","volume":"10","author":"Wu","year":"2023","journal-title":"Front. Mar. Sci."},{"key":"ref_31","first-page":"043103","article-title":"Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine","volume":"57","author":"Wang","year":"2018","journal-title":"Opt. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_33","unstructured":"Wang, D., Holmes, M., Vinson, R., Seibel, G., and Tao, Y. (August, January 29). Machine Vision Guided Robotics for Blue Crab Disassembly\u2014Deep Learning Based Crab Morphology Segmentation. Proceedings of the ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers, Detroit, MI, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chelouati, N., Bouslimani, Y., and Ghribi, M. (2023). Lobster Position Estimation Using YOLOv7 for Potential Guidance of FANUC Robotic Arm in American Lobster Processing. Designs, 7.","DOI":"10.3390\/designs7030070"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1093\/icesjms\/fsaa250","article-title":"Learning-based low-illumination image enhancer for underwater live crab detection","volume":"78","author":"Cao","year":"2021","journal-title":"ICES J. Mar. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1093\/icesjms\/fsz223","article-title":"Automatic detection of Western rock lobster using synthetic data","volume":"77","author":"Mahmood","year":"2020","journal-title":"ICES J. Mar. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chelouati, N., Fares, F., Bouslimani, Y., and Ghribi, M. (2021, January 28\u201329). Lobster detection using an Embedded 2D Vision System with a FANUC industrual robot. Proceedings of the 2021 IEEE International Symposium on Robotic and Sensors Environments (ROSE), Virtual Conference.","DOI":"10.1109\/ROSE52750.2021.9611755"},{"key":"ref_38","first-page":"478","article-title":"Computer vision identification of species, sex, and age of indonesian marine lobsters","volume":"9","author":"Hasan","year":"2021","journal-title":"INFOKUM"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/raq.12726","article-title":"Deep learning for visual recognition and detection of aquatic animals: A review","volume":"15","author":"Li","year":"2023","journal-title":"Rev. Aquac."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Juan","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A survey of deep learning-based object detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 19\u201321). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_46","unstructured":"(2024, January 12). Available online: https:\/\/pypi.org\/project\/labelImg\/."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Na, S., Xumin, L., and Yong, G. (2010, January 2\u20134). Research on k-means clustering algorithm: An improved k-means clustering algorithm. Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, Jian, China.","DOI":"10.1109\/IITSI.2010.74"},{"key":"ref_48","unstructured":"(2024, January 12). Available online: https:\/\/www.learnbymarketing.com\/methods\/k-means-clustering\/."},{"key":"ref_49","unstructured":"(2024, April 09). seafish.co.uk. Available online: https:\/\/seafish.org."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/5\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:44:22Z","timestamp":1760107462000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/5\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,11]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["computers13050119"],"URL":"https:\/\/doi.org\/10.3390\/computers13050119","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,11]]}}}