A Review on Methods of Identifying and Counting Aedes Aegypti Larvae using Image Segmentation Technique

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing

Ship Hull Repair Using A Swarm Of Autonomous Underwater Robots: A Self-Assembly Algorithm

When ships suffer hull damage at sea, quick and effective repairs are vital. In these scenarios where even minutes make a substantial difference, repair crews need every edge they can get. In this paper, we propose a self-assembly algorithm to be used by a homogeneous swarm of autonomous underwater robots to aggregate at the hull breach and use their bodies to form a patch of appropriate size to cover the hole. Our approach is inspired by existing modular robot technologies and techniques, which are used to justify the feasibility of the proposed system presented in this paper. We test the ability of the agents to form a patch for various breach sizes and location and investigate the effect of varying population density. The system is verified within the two-dimensional Netlogo simulation environment and shows how the system performance can be quantified in relation to the sizes of the breach and the swarm. The methodology and simulation results illustrate that the swarm robot approach presented in this paper forms an important contribution in the emergency ship hull repair scenario and compares much advantageously against the traditional shoring methods. We conclude by suggesting how the approach may be extended to a three-dim

CLASSIFICATION OF LARGE SCALE FISH DATASET BY DEEP NEURAL NETWORKS

The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of 100% over the Large-scale fish dataset, with the potential for real-time deployment in aquatic monitoring systems. Furthermore, this work proposes avenues for future research in the domain of fish classification

A Review On Methods Of Identifying And Counting Aedes Aegypti Larvae Using Image Segmentation Technique

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing

Deep learning based deep-sea automatic image enhancement and animal species classification

The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers.This work was developed at Deusto Seidor S.A. (01015, Vitoria-Gasteiz, Spain) within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring); MarTERA ERA-Net Cofund; Centro para el Desarrollo Tecnológico Industrial, CDTI; and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades). This work was supported by the Centro para el Desarrollo Tecnológico Industrial (CDTI) (Grant No. EXP 00108707 / SERA-20181020)

A review on the use of computer vision and artificial intelligence for fish recognition, monitoring, and management.

Abstract: Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine the best course of action for new studies. In this context, this article characterizes the current state of the art by identifying the main studies on the subject and briefly describing their approach. In contrast with most previous reviews related to technology applied to fish recognition, monitoring, and management, rather than providing a detailed overview of the techniques being proposed, this work focuses heavily on the main challenges and research gaps that still remain. Emphasis is given to prevalent weaknesses that prevent more widespread use of this type of technology in practical operations under real-world conditions. Some possible solutions and potential directions for future research are suggested, as an effort to bring the techniques developed in the academy closer to meeting the requirements found in practice

水中調査自動化のための画像処理技術に関する研究

九州工業大学博士学位論文 学位記番号:生工博甲第287号 学位授与年月日:平成29年3月24日1.序論|2.水中画像補正技術|3.興味画像の選択|4.水中画像の圧縮と復元|5.実海域での画像送信実験|6.結論九州工業大学平成28年