Run the command (linux) to evaluate map for a YOLO-Fish models. A robust fish detection model for real-time underwater fish detection in any marine environments.

Yalelo is the pioneer of aquaculture in Zambia, bringing together local resources and world-leading practices. Founded in 2011, we have grown quickly to become the leading fisheries business in Zambia and the largest producer of freshwater fish in Sub-Saharan Africa.

Dec 1, 2022 · In this paper, we propose YOLO-Fish, a deep learning based fish detection model. We have proposed two models, YOLO-Fish-1 and YOLO-Fish-2. YOLO-Fish-1 enhances YOLOv3 by fixing the issue of upsampling step sizes of to reduce the misdetection of tiny fish.

This study contributes to the advancement of underwater fish detection by showcasing the suitability of the CFC dataset and the efficacy of YOLO v7 and YOLO v8 models. These insights can pave the way for further advancements in fish detection, supporting conservation efforts and sustainable fisheries management.

Sep 23, 2024 · To address these issues, we propose a fish detection algorithm—FishDet-YOLO—through improvement in the YOLOv8 algorithm. To tackle the complexities of underwater environments, we design an Underwater Enhancement Module network (UEM) that can be jointly trained with YOLO.

We grow tilapia fish in the clear open waters of Lake Victoria. Our cages are among the largest in Africa, providing an open environment for our fish to swim far and deep against natural lake currents.

This repository implements YOLOv3 and DeepSORT for tracking and counting of 2 different fish species in an aquarium. YOLO (You only look once) uses CNN to detect objects in real time.

This project focuses on the detection and tracking of fish in images using the YOLO (You Only Look Once) object detection model. The primary goal is to identify fish in various images, annotate them with bounding boxes, and understand their movement patterns.

Jan 3, 2021 · In this paper, we provide a fish farm monitoring system that is based on a combination of algorithms to detect fish count and trajectories. Firstly, we enhance turbid underwater images by Multi-scale Retinex algorithm (Petro et al. 2014) which makes it …

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