Fish Species Identification in Real-Life Underwater Images

Palazzo, Simone; Murabito, Francesca

doi:10.1145/2661821.2661822

Cited by 16 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The project resulted to 87K hours of video (95 TB) and 145 million fish identifications. It has then made the successfully curated database available to the rest of the world and most of the developments in automatic classification and identification tools for fishes have used the database to train deep learning models (see in Table 4 uses of F4K: Spampinato et al, 2010;Palazzo and Murabito, 2014;Shafait et al, 2016;Jalal et al, 2020;Murugaiyan et al, 2021). For temperate fishes, only a few commercial species can be automatically identified by existing models but are nonetheless gaining more recognition.…”

Section: Transfer Learning For Data-deficient Environmentsmentioning

confidence: 99%

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

2023

View full text Add to dashboard Cite

Through the advancement of observation systems, our vision has far extended its reach into the world of fishes, and how they interact with fishing gears—breaking through physical boundaries and visually adapting to challenging conditions in marine environments. As marine sciences step into the era of artificial intelligence (AI), deep learning models now provide tools for researchers to process a large amount of imagery data (i.e., image sequence, video) on fish behavior in a more time-efficient and cost-effective manner. The latest AI models to detect fish and categorize species are now reaching human-like accuracy. Nevertheless, robust tools to track fish movements in situ are under development and primarily focused on tropical species. Data to accurately interpret fish interactions with fishing gears is still lacking, especially for temperate fishes. At the same time, this is an essential step for selectivity studies to advance and integrate AI methods in assessing the effectiveness of modified gears. We here conduct a bibliometric analysis to review the recent advances and applications of AI in automated tools for fish tracking, classification, and behavior recognition, highlighting how they may ultimately help improve gear selectivity. We further show how transforming external stimuli that influence fish behavior, such as sensory cues and gears as background, into interpretable features that models learn to distinguish remains challenging. By presenting the recent advances in AI on fish behavior applied to fishing gear improvements (e.g., Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), coupled networks), we discuss the advances, potential and limits of AI to help meet the demands of fishing policies and sustainable goals, as scientists and developers continue to collaborate in building the database needed to train deep learning models.

show abstract

Section: Transfer Learning For Data-deficient Environmentsmentioning

confidence: 99%

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

2023

View full text Add to dashboard Cite

show abstract

“…In the paper of Palazzo and Murabito [5], a semi-supervised approach for fine-grained recognition is presented, which hits one of the most massive datasets in the multimediaecological field: we are talking about 20M of fish images, taken in an underwater scenario. This would certainly be one of the most intriguing challenge for verifying how fine-grained recognition systems are capable of doing their job, and identifying subtle differences among diverse species, whose image instances are taken in a noisy, real scenario.…”

Section: Descriptionmentioning

confidence: 99%

Summary Abstract for the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data

Spampinato

Mezaris

Cristani

2014

Proceedings of the 22nd ACM International Conference on Multimedia

View full text Add to dashboard Cite

The 3 rd ACM International Workshop on Multimedia Analysis for Ecological Data (MAED'14) is held as part of ACM Multimedia 2014. MAED'14, following the previous two workshops of the MAED series (MAED'12 and MAED'13) held as part of, respectively, ACM Multimedia 2012 and ACM Multimedia 2013, is concerned with the processing, interpretation, and visualization of ecology-related multimedia content with the aim to support biologists in their investigations for analyzing and monitoring natural environments.

show abstract

“…Previously, numerous image and video processing algorithms were used in underwater biological detection. Palazzo et al used Efficient Match Kernels (EKM) and Kernel Descriptors (KDES) as fish features and trained a multi-class SVM classifier to achieve excellent detection results [6]. Using the Spatial Pyramid Pooling (SPP) algorithm to extract invariant features and a linear Support Vector Machine (SVM) classifier for classification, Qin et al [7] proposed a method for detecting deep-sea fish.…”

Section: Introductionmentioning

confidence: 99%

High Speed and Precision Underwater Biological Detection Based on the Improved YOLOV4-Tiny Algorithm

Cheng

Tian

et al. 2022

JMSE

View full text Add to dashboard Cite

Realizing high-precision real-time underwater detection has been a pressing issue for intelligent underwater robots in recent years. Poor quality of underwater datasets leads to low accuracy of detection models. To handle this problem, an improved YOLOV4-Tiny algorithm is proposed. The CSPrestblock_body in YOLOV4-Tiny is replaced with Ghostblock_body, which is stacked by Ghost modules in the CSPDarknet53-Tiny backbone network to reduce the computation complexity. The convolutional block attention module (CBAM) is integrated to the algorithm in order to find the attention region in scenarios with dense objects. Then, underwater data is effectively improved by combining the Instance-Balanced Augmentation, underwater image restoration, and Mosaic algorithm. Finally, experiments demonstrate that the YOLOV4-Tinier has a mean Average Precision (mAP) of 80.77% on the improved underwater dataset and a detection speed of 86.96 fps. Additionally, compared to the baseline model YOLOV4-Tiny, YOLOV4-Tinier reduces about model size by about 29%, which is encouraging and competitive.

show abstract

Fish Species Identification in Real-Life Underwater Images

Cited by 16 publications

References 19 publications

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

Summary Abstract for the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data

High Speed and Precision Underwater Biological Detection Based on the Improved YOLOV4-Tiny Algorithm

Contact Info

Product

Resources

About