#clahe

[oaresearchpaper]

Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.

[call-me-rucy]

Wdym there's not an implementation of this stupidly specific algorythm in this programing language that almost no one uses.

why doesnt adapthisteq work on octave? Long shot but does anyone perchance have at hand an implemantation of CLAHE that runs in Octave without errors and in less than a minute???

Cause nooooo we couldnt do this in Matlab, or R, or even python...

[oaresearchpaper]

Nile Tilapia Count and Location: AI and CLAHE Unleashed

Abstract

Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.

Introduction

Accurate counting of organisms, such as Nile Tilapia (Oreochronis niloticus), is important for various applications, including fisheries management, environmental monitoring, and aquaculture operations (Li et al., 2020). In the Philippines, tilapia is the second most important cultured species, with approximately 281,111 MT of total production in 2021. In 2020, tilapia made up 20% of the aquaculture production in the country, with Central Luzon as the leading producer. Tilapia is an important commodity for food security and economic development (PCAARRD, n.d.).

The tilapia industry in the Philippines has made notable growth in production from 2002 to 2022, with an increase of 115.58%. This may be attributed to several programs done by the government, such as improving the strain of tilapia and improving the technology in production and culture to sustain industry growth (Bureau of Fisheries and Aquatic Resources, 2022).

However, despite the progress made in tilapia aquaculture, problems and challenges persist. Pollutionrelated problems like diseases and water quality management, sources of quality fingerlings, and market competition are among the key challenges faced by farmers (Bureau of Fisheries and Aquatic Resources, 2022). Addressing these challenges and enhancing the efficiency and sustainability of tilapia production is crucial for the industry's continued growth.

In this context, developing an automated methodology for accurate surface tilapia detection using a Convolutional Neural Network (CNN) brings an opportunity to improve tilapia farming practices. Leveraging the capabilities of CNN and Contrast Limited Adaptive Histogram Equalization (CLAHE) aims to develop an approach to determine whether Nile Tilapia are at the surface or submerged. The insights gained from this research can contribute to optimizing feeding strategies, improving management practices, and enhancing tilapia aquaculture's overall productivity.

The paper is presented as follows: Section 1 introduces the motivation for the research. Section 2 concentrated on the related works on image processing, CNN, and CLAHE. The methodology of the research is presented in Section 3. Section 4 presents the Tests and Results. Finally, Section 5 discussed the conclusion and future works.

Source : Nile Tilapia Count and Location: AI and CLAHE Unleashed | InformativeBD

[oaresearchpaper]

Nile Tilapia Count and Location: AI and CLAHE Unleashed | InformativeBD

Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.

[oaresearchpaper]

Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.