A YOLO-Based Approach for Aedes Aegypti Larvae Classification and Detection

Hubalde, Angelo M.; Padilla, Dionis A.; Santos, Denzel Antoinette C.

doi:10.1109/icivc52351.2021.9527017

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…However, other pre-trained object detection algorithms, such as YOLO, not only identify objects but also provide their location within the image. This feature is crucial in tasks such as counting similar objects and distinguishing among various items [ 21 ]. YOLO is a unified object model that does not require a separate network to extract candidate regions, which makes it simple to configure and suitable for real-world applications by directly training the entire image [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

RT-CLAD: Artificial Intelligence-Based Real-Time Chironomid Larva Detection in Drinking Water Treatment Plants

Jang,

Yeo,

Park

et al. 2023

Sensors

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The presence of chironomid larvae in tap water has sparked public concern regarding the water supply system in South Korea. Despite ongoing efforts to establish a safe water supply system, entirely preventing larval occurrences remains a significant challenge. Therefore, we developed a real-time chironomid larva detection system (RT-CLAD) based on deep learning technology, which was implemented in drinking water treatment plants. The acquisition of larval images was facilitated by a multi-spectral camera with a wide spectral range, enabling the capture of unique wavelet bands associated with larvae. Three state-of-the-art deep learning algorithms, namely the convolutional neural network (CNN), you only look once (YOLO), and residual neural network (ResNet), renowned for their exceptional performance in object detection tasks, were employed. Following a comparative analysis of these algorithms, the most accurate and rapid model was selected for RT-CLAD. To achieve the efficient and accurate detection of larvae, the original images were transformed into a specific wavelet format, followed by preprocessing to minimize data size. Consequently, the CNN, YOLO, and ResNet algorithms successfully detected larvae with 100% accuracy. In comparison to YOLO and ResNet, the CNN algorithm demonstrated greater efficiency because of its faster processing and simpler architecture. We anticipate that our RT-CLAD will address larva detection challenges in water treatment plants, thereby enhancing water supply security.

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Section: Methodsmentioning

confidence: 99%

RT-CLAD: Artificial Intelligence-Based Real-Time Chironomid Larva Detection in Drinking Water Treatment Plants

Jang,

Yeo,

Park

et al. 2023

Sensors

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“…YOLOv4 is used by both Genaev et al [27] and Chen et al [34], where the former uses the regular version and the latter [30] uses the tiny version. Finally, several authors [10], [29], [31], [81], [82] use YOLOv3, also known by the use of Dark-Net backbone. We also notice that YOLO has been used in combination with other classification techniques, such as Takimoto et al [14] used YOLOv4 and Efficient-Net, and Kuzuhara et al [8] used YOLOv3 and Xception (proposed by Chollet et al [83]), and Liu et al [43] used YOLOv3 with Global Context (GC) Network (proposed by Cao et al [84]).…”

Section: Existing Detection Techniquesmentioning

confidence: 99%

“…-Some articles only detect one insect or one class: [9], [19], [23], [30], [41], [42], [44], [50], [52], [82], [90], -Others detect two classes: [8], [14], [26], [46], -Other papers detect 3 classes: [7], [20], [27], [28], [32], [48], [51], -Other articles detect 4 classes: [17], and [89], -Some of the studied papers detect 5 classes: [35], [10], and [39], -There are some papers that detect 6 classes: [15], [16], [18], [25], [34], -Other papers detect from 7 to 9 classes: [24] (7 classes), [31], [37], and [85] Since there are fewer studies as the number of insects increases, 66% of all articles focus on insects with a population of one to six.…”

Section: Existing Classification Techniquesmentioning

confidence: 99%

A Systematic Literature Review on Insect Detection in Images

Souifi¹,

Mdhaffar²,

Rodriguez³

et al. 2022

Signal, Image Processing and Embedded Systems Trends

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Due to the advancements of deep learning (DL), particularly in the areas of visual object detection and convolutional neural networks (CNN), insect detecetion in images has received a lot of attention from the research community in the last few years. This paper presents a systematic review of the literature on the topic of insect detection as a case of object detection in images. It covers 50 research papers on the subject and responds to three research questions: i) type of dataset used; ii) detection technique used; iii) insect location. The paper also provides a summary of existing methods used for insect detection.

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