Detection of mold on the food surface using YOLOv5

Jubayer, Md. Fahad; Soeb, Janibul Alam; Mojumder, Abu Naser; Paul, Mitun Kanti; Barua, Pranta; Kayshar, Shahidullah; Akter, Syeda Sabrina; Rahman, Mizanur; Islam, Amirul

doi:10.1016/j.crfs.2021.10.003

Cited by 68 publications

(33 citation statements)

References 7 publications

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“…In the proposed method, a state-of-the-art and light weighted detection method, YOLO-V5, is adopted to detect lettuces (Jubayer et al, 2021;Zhao et al, 2021;Wang et al, 2022). Then, we can get the bounding box of each object in one frame and calculate the center point of each bounding box.…”

Section: Feature Extractionmentioning

confidence: 99%

LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture

Wang

et al. 2022

Front. Plant Sci.

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The precision spray of liquid fertilizer and pesticide to plants is an important task for agricultural robots in precision agriculture. By reducing the amount of chemicals being sprayed, it brings in a more economic and eco-friendly solution compared to conventional non-discriminated spray. The prerequisite of precision spray is to detect and track each plant. Conventional detection or segmentation methods detect all plants in the image captured under the robotic platform, without knowing the ID of the plant. To spray pesticides to each plant exactly once, tracking of every plant is needed in addition to detection. In this paper, we present LettuceTrack, a novel Multiple Object Tracking (MOT) method to simultaneously detect and track lettuces. When the ID of each plant is obtained from the tracking method, the robot knows whether a plant has been sprayed before therefore it will only spray the plant that has not been sprayed. The proposed method adopts YOLO-V5 for detection of the lettuces, and a novel plant feature extraction and data association algorithms are introduced to effectively track all plants. The proposed method can recover the ID of a plant even if the plant moves out of the field of view of camera before, for which existing Multiple Object Tracking (MOT) methods usually fail and assign a new plant ID. Experiments are conducted to show the effectiveness of the proposed method, and a comparison with four state-of-the-art Multiple Object Tracking (MOT) methods is shown to prove the superior performance of the proposed method in the lettuce tracking application and its limitations. Though the proposed method is tested with lettuce, it can be potentially applied to other vegetables such as broccoli or sugar beat.

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Section: Feature Extractionmentioning

confidence: 99%

LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture

Wang

et al. 2022

Front. Plant Sci.

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“…YOLOv5 is a single-stage object detection model made up of four components: Input, Backbone, Neck, and Head. In comparison to the original YOLOv4 network model, this model adds the Focus module [10], at the same time, it also adopts the improved the Cross Stage Partial Network (CSPNet) as the backbone of the network to extract image features, a bottom-up Path Aggregation Network (PANet) layer based on the Feature Pyramid Structure (FPN) is also added in to strengthen the multi-scale feature fusion mechanism [11]. The structure of this model is shown in Fig.…”

Section: Principles Of Yolov5 Algorithmmentioning

confidence: 99%

“…The most widely used assessment index in object detection is the mAP, which is determined by taking the accuracy rate of each single category when IoU = 0.5 [25], [26]. Each indicator's calculating formula is given in (10) to (12):…”

Section: B Experimental Parameter Setting and Evaluation Indexmentioning

confidence: 99%

Investigation Into Recognition Algorithm of Helmet Violation Based on YOLOv5-CBAM-DCN

et al. 2022

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Recognition of safety helmets wearing by construction workers is a common target detection topic in applications of deep learning-based image processing. This paper provides a study of an enhanced YOLOv5-based method, in which the challenges caused by complicated construction environment backgrounds, dense targets, and the irregular shape of safety helmets are addressed. In a trunk network, feature extraction is more based on the target shape by using the Deformable Convolution Net instead of the conventional convolution; in the Neck, a Convolutional Block Attention Module is introduced to weaken feature extraction of complex backgrounds by giving weights to enhance the characterization ability of target features; and the original network's Generalized Intersection over Union Loss is replaced by Distance Intersection over Union Loss to overcome the problem of erroneous location when the population is dense. The dataset for the training network is created by mixing open-source datasets with autonomous collecting to evaluate the effectiveness of the algorithm. We observed that the improved model has a detection accuracy of 91.6%, up 2.3% over the original network model, and a detection speed of 29 frames per second, which is compliant with most security cameras' capture frame rate.

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“…With the continuous development of deep learning technology in recent years, the target detection technology has wide application in various industries due to its good indepth feature perception capability [8][9][10][11][12]. Among the applications in agriculture for example, apple-related recognition [13][14][15], tomato recognition [16][17][18] and crop disease recognition [20], [21], etc.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Object Detection Scheme by Improving YOLOv5 for Sprouted Potatoes Datasets

et al. 2022

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Detection of mold on the food surface using YOLOv5

Cited by 68 publications

References 7 publications

LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture

LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture

Investigation Into Recognition Algorithm of Helmet Violation Based on YOLOv5-CBAM-DCN

A Deep Learning-Based Object Detection Scheme by Improving YOLOv5 for Sprouted Potatoes Datasets

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