Automatic Fish Detection from Different Marine Environments Video Using Deep Learning (original) (raw)
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Fish Detection Using Deep Learning
Applied Computational Intelligence and Soft Computing, 2020
Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being’s learning r...
2020
Environmental monitoring guides conservation, and is thus particularly important for coastal aquatic habitats, which are heavily impacted by human activities. Underwater cameras and unmanned devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce three deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). Two were trained on footage from single habitats (seagrass or reef), and one on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively), but p...
Fish species classification in unconstrained underwater environments based on deep learning
Limnology and Oceanography: Methods, 2016
Underwater video and digital still cameras are rapidly being adopted by marine scientists and managers as a tool for non-destructively quantifying and measuring the relative abundance, cover and size of marine fauna and flora. Imagery recorded of fish can be time consuming and costly to process and analyze manually. For this reason, there is great interest in automatic classification, counting, and measurement of fish. Unconstrained underwater scenes are highly variable due to changes in light intensity, changes in fish orientation due to movement, a variety of background habitats which sometimes also move, and most importantly similarity in shape and patterns among fish of different species. This poses a great challenge for image/video processing techniques to accurately differentiate between classes or species of fish to perform automatic classification. We present a machine learning approach, which is suitable for solving this challenge. We demonstrate the use of a convolution neural network model in a hierarchical feature combination setup to learn species-dependent visual features of fish that are unique, yet abstract and robust against environmental and intra-and inter-species variability. This approach avoids the need for explicitly extracting features from raw images of the fish using several fragmented image processing techniques. As a result, we achieve a single and generic trained architecture with favorable performance even for sample images of fish species that have not been used in training. Using the LifeCLEF14 and LifeCLEF15 benchmark fish datasets, we have demonstrated results with a correct classification rate of more than 90%.
Fish Species Classification from Underwater Images using Large-Scale Dataset via Deep Learning
Research Square (Research Square), 2022
Many natural science investigations, including fishery assessment, marine environment assessment, and environmental research, depend on the classification of underwater fish species from apprehended images in habitats. But due to noisy captured images, existing models faced lots of issues during recognition of fish species from the underwater images and to solvu such kind of problem, need to develop a high-performance fish recognition model, although it can be challenging due to the chaotic nature of underwater imagery. In directive to train Deep Neural Network (DNN) in precise manner to develop a Fish Species Classification (FSC) model from noisy large-scale underwater captured images, this research article introduces an inimitable deep learning framework called Optimized DNN. The underwater environment is very fascinating and challenging and many research groups are currently working together to unravel the facts of underwater imaging and mapping. The proposed FSC model is use for fish detection based on the segmentation approach and DNN-based identification in complex underwater environments. To simulate and validate the proposed FSC model, publicly available Fish4Knowledge (Fish Detection) benchmark dataset is used and experimental results show that the FSC model performance is far better in terms of Average Precision (AP = 92.26%), Average Recall (AR = 87.45%), Average F-measure (AF = 89.74%) and Average Accuracy (AA = 93.86%). The composite FSC network model increases the usage of distinctive info and the output of distinctive info for the discovered object.
Robust Underwater Fish Detection Using an Enhanced Convolutional Neural Network
International Journal of Image, Graphics and Signal Processing, 2021
Underwater Object Detection is one of the most challenging and unexplored domains in this area of Computer Vision. The proposed research refines the image enhancement of underwater imagery by proposing an improvement of already existing tools for underwater Object detection. The comparative study clearly depicts the enhancement of the proposed method with respect to the existing methods for underwater object detection. Moreover, a framework for detection of underwater organisms such as fishes are proposed, which will act as the steppingstone for reserving the ecosystem of the whole fish community. Mostly the object detection using deep learning has been the prime goal to this research and the comparison between other preexisting methods are compared at the end. As a result, techniques that are already well established will be used for overall enhancement of those images. Through this enhancement and through finding a healthy environment for their breeding ground, the extinction of selected species of fishes is can be diminished and decreased. All this is carried out by overcoming difficulties underwater through a novel technique that can be integrated into an Underwater Autonomous Vehicle and can be classified as robust in nature. Robustness will depend on three important factors in this research, first is accuracy, then fast and lastly being upgradeable. The proposed method is a modified VGGNet-16, which is trained using the ImageCLEF FISH_TS dataset. The overall result provides an accuracy of 96.4% which surpasses all its predecessors.
Ecological Informatics, 2018
Identifying and counting fish individuals on photos and videos is a crucial task to cost-effectively monitor marine biodiversity, yet it remains difficult and time-consuming. In this paper, we present a method to assist the identification of fish species on underwater images, and we compare our model performances to human ability in terms of speed and accuracy. We first tested the performance of a convolutional neural network (CNN) trained with different photographic databases while accounting for different post-processing decision rules to identify 20 fish species. Finally, we compared the performance of species identification of our best CNN model with that of humans on a test database of 1197 fish images representing nine species. The best CNN was the one trained with 900 000 images including (i) whole fish bodies, (ii) partial fish bodies and (iii) the environment (e.g. reef bottom or water). The rate of correct identification was 94.9%, greater than the rate of correct identification by humans (89.3%). The CNN was also able to identify fish individuals partially hidden behind corals or behind other fish and was more effective than humans to identify fish on smallest or blurry images while humans were better to identify fish individuals in unusual positions (e.g. twisted body). On average, each identification by our best CNN using a common hardware took 0.06 seconds. Deep Learning methods can thus perform efficient fish identification on underwater images and offer promises to build-up new video-based protocols for monitoring fish biodiversity cheaply and effectively.
Ecological Informatics, 2020
It is important for marine scientists and conservationists to frequently estimate the relative abundance of fish species in their habitats and monitor changes in their populations. As opposed to laborious manual sampling, various automatic computer-based fish sampling solutions in underwater videos have been presented. However, an optimal solution for automatic fish detection and species classification does not exist. This is mainly because of the challenges present in underwater videos due to environmental variations in luminosity, fish camouflage, dynamic backgrounds, water murkiness, low resolution, shape deformations of swimming fish, and subtle variations between some fish species. To overcome these challenges, we propose a hybrid solution to combine optical flow and Gaussian mixture models with YOLO deep neural network, an unified approach to detect and classify fish in unconstrained underwater videos. YOLO based object detection system are originally employed to capture only the static and clearly visible fish instances. We eliminate this limitation of YOLO to enable it to detect freely moving fish, camouflaged in the background, using temporal information acquired via Gaussian mixture models and optical flow. We evaluated the proposed system on two underwater video datasets i.e., the LifeCLEF 2015 benchmark from the Fish4Knowledge repository and a dataset collected by The University of Western Australia (UWA). We achieve fish detection F-scores of 95.47% and 91.2%, while fish species classification accuracies of 91.64% and 79.8% on both datasets respectively. To our knowledge, these are the best reported results on these datasets, which show the effectiveness of our proposed approach.
Frontiers in Marine Science
Machine-assisted object detection and classification of fish species from Baited Remote Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an opportunity for optimising analysis time and rapid reporting of marine ecosystem statuses. Training object detection algorithms for BRUVS analysis presents significant challenges: the model requires training datasets with bounding boxes already applied identifying the location of all fish individuals in a scene, and it requires training datasets identifying species with labels. In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. Here, we present a “machine-assisted” approach for i) a generalised model to automate the application of bounding boxes to any underwater environment containing fish and ii) fish detection and classification...
2019
Aquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricu...
DEEP CONVOLUTIONAL NETWORKS FOR UNDERWATER FISH LOCALIZATION AND SPECIES CLASSIFICATION
IAEME PUBLICATON, 2020
Live fish recognition is a difficult multi-class order task in the open sea. We propose a technique to perceive fish in an unlimited common habitat.In the proposed technique, VGG-16 with deep fish architecture is used to enhance the feature extraction what's more, to improve the exactness of the result.The proposed approach comprises of two fundamental stages; namely Fish Localization phase and Fish classification phase.The technique first detect the fish from the image by extracting feature map using VGG16 network. DeepFish architectureis used to categorize the Fish.Then, the proposed approach uses support vector machine and random forest classifier to differentiate between fish species. Experimental results obtained show that VGG16 with deepfish architecture using support vector machine attains a better accuracy of 99.47%.