Fish Detection and Classification Using Convolutional Neural Networks

Rekha, B S; Srinivasan, Gopalakrishnan; Reddy, Sravan Kumar; Kakwani, Divyanshu; Bhattad, Niraj

doi:10.1007/978-3-030-37218-7_128

Cited by 23 publications

(10 citation statements)

References 14 publications

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“…Rekha et al used CNN for fish detection and had an accuracy rate of 90%. However, they used images of the camera feeds from fishing boats, and the dataset was populated with fish outside the water [ 47 ]. Research by Christensen et al indicated a low fish detection accuracy due to the complexity and clearness of the selected dataset images [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish

Manawadu

Zoysa

Perera

et al. 2023

Sensors

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Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in this approach is controlling the movement of fish to facilitate human interaction. This study proposed an interactive robot, a robotic fish, to alter fish swarm behaviors by performing an effective, unobstructed, yet necessary, defined set of actions to enhance human interaction. The approach incorporated a minimalistic but futuristic physical design of the robotic fish with cameras and infrared (IR) sensors, and developed a fish-detecting and swarm pattern-recognizing algorithm. The fish-detecting algorithm was implemented using background subtraction and moving average algorithms with an accuracy of 78%, while the swarm pattern detection implemented with a Convolutional Neural Network (CNN) resulted in a 77.32% accuracy rate. By effectively controlling the behavior and swimming patterns of fish through the smooth movements of the robotic fish, we evaluated the success through repeated trials. Feedback from a randomly selected unbiased group of subjects revealed that the robotic fish improved human interaction with fish by using the proposed set of maneuvers and behavior.

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Section: Resultsmentioning

confidence: 99%

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish

Manawadu

Zoysa

Perera

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, these shallow learning methods do not scale with data. Because of the deep layer structure and massive data support, the performance of DL approaches is higher than the shallow learning methods [ 29 , 30 , 31 ].…”

Section: Related Workmentioning

confidence: 99%

Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset

Alaba

Nabi

Shah

et al. 2022

Sensors

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Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head. Moreover, a class-aware loss function is proposed to solve the class imbalance problem of our dataset. The class-aware loss takes the number of instances in each species into account and gives more weight to those species with a smaller number of instances. This loss function can be applied to any classification or object detection task with an imbalanced dataset. The experimental result on the large-scale reef fish dataset, SEAMAPD21, shows that the class-aware loss improves the model over the original loss by up to 79.7%. The experimental result on the Pascal VOC dataset also shows the model outperforms the original SSD object detection model.

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“…Detect and classify fish [13] Convolutional neural networks Not reported [27] Convolutional neural networks 90% and 92% for detection and classification Fish detection / recognition [11] AI techniques and computer vision 90.9% for CNN, 87.08 -98.67% for SURF…”

Section: Reference Techniques Accuracymentioning

confidence: 99%

Fish Species Identification Techniques: A Review

Hassoon¹

2022

ANJS

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Fish species identification process is still problematical and time consuming, so automated fish identification is important. The performance of various fish identification techniques is compared depending on pre-processing, number of significant characteristics and identification correctness. The reason for writing this review stems from the importance of fish for humans as food and as a wealth which support the economy of countries. Therefore, it is necessary to shed light on fish and how to distinguish the beneficial and harmful ones, discover and identify fish diseases, and so on, which contributes in building a knowledge base available to all. The aim of this paper is present a review on various fish species identification techniques; pre-processing operations, features extraction methods, recognition/classification techniques and databases. This review will be helpful for beginner researchers, it can be used as a starting point for new fish species identification approaches.

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Fish Detection and Classification Using Convolutional Neural Networks

Cited by 23 publications

References 14 publications

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish

Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset

Fish Species Identification Techniques: A Review

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