Sibayan, Christian JoseAbando, Benjie R.Acosta, Justin P.Madrelijos, Johnico D.Marzan, Charlie S.2026-08-202026-08-202024Sibayan, C. J., Abando, B. R., Madrelijos, J. D., Acosta, J. P., & Marzan, C. S. (2024).Deep learning-assisted milkfish fingerling counting: Towards automation in fish industry practices. ACM International Conference Proceeding Series, 232-236. https://doi.org/10.1145/3674558.3674591https://lakasa.dmmmsu.edu.ph/handle/123456789/2407Full text.The manual counting of milkfish fingerlings within the fish industry in the Philippines poses significant challenges due to its time-consuming nature, labor intensiveness, and susceptibility to errors. To address these issues, this study presents a deep learning approach for the real-time counting of milkfish fingerlings to enhance both the speed and accuracy of fingerling counts. The dataset was meticulously curated from six milkfish fingerlings farms, with gathered videos converted into image frames and subjected to polygon annotation. Furthermore, various augmentation techniques were applied to these images to enhance training efficacy before integration into a deep neural network. Notably, implementing the YOLOv8n-seg object detector with segmentation and counting yielded a remarkable accuracy rate of up to 90.64% and 93.34%, respectively. This innovative methodology promises significant advancements in the automation and efficiency of milkfish fingerling counting processes within the fish industry, paving the way for improved productivity and accuracy in fishery management practices. © 2024 ACM.enComputer visionDeep learningYOLOv8Image segmentationImage preprocessingObject detectionAquacultureMilkfishFingerlings counting006.4Computer visionImage processing--Computer programsNeural networks (Computer science)Object recognition (Computer vision)AquacultureComputer visionImage processing--Computer programsNeural networks (Computer science)Object recognition (Computer vision)AquacultureMilkfishArticle