Fruits and Vegetables Recognition using YOLO

Latha, R.; Sreekanth, G. R.; Rajadevi, R.; Nivetha, S.; Kumar, K.Ajith; Akash, V.; Bhuvanesh, Singasani P; Anbarasu, Pon

doi:10.1109/iccci54379.2022.9740820

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…There are a lot of methods for fruit object detection [2,4,15,18,33]. The fruit detection essentially needs both classification and localization by providing the class labels and bounding box coordinates of the targets [8,14,16,26,27]. Visual object detection is to use YOLOv8 model or CenterNet model and dataset composed of ground truths, so that the model can extract visual features of the fruits, and then output the predicted results.…”

Section: Fruit Detectio Nmentioning

confidence: 99%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

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Deep learning-based visual object detection is a fundamental aspect of computer vision. These models not only locate and classify multiple objects within an image, but they also identify bounding boxes. The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Our proposed model extracts visual features from fruit images and analyzes fruit peel characteristics to predict the fruit's class. We utilize our own datasets to train two "anchor-free" models: YOLOv8 and CenterNet, aiming to produce accurate predictions. The CenterNet network primarily incorporates ResNet-50 and employs the deconvolution module DeConv for feature map upsampling. The final three branches of convolutional neural networks are applied to predict the heatmap. The YOLOv8 model leverages CSP and C2f modules for lightweight processing. After analyzing and comparing the two models, we found that the C2f module of the YOLOv8 model significantly enhances classification results, achieving an impressive accuracy rate of 99.5%.

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Section: Fruit Detectio Nmentioning

confidence: 99%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

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“…For image recognition, the deployment of neural networks in hardware generally involves GPUs, ensuring the algorithm's implementation and fast recognition speed, but at the cost of higher power and resource consumption [1]. In [10], the neural network is deployed in hardware, and image segmentation is performed using a bounding box regression algorithm. Deploying the neural network in hardware implementation can effectively enhance the real-time performance of the system.…”

Section: Visual Sectionmentioning

confidence: 99%

An FPGA-Based Hardware Low-Cost, Low-Consumption Target-Recognition and Sorting System

Wang,

Han,

Chen

et al. 2023

WEVJ

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In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

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“…For the detection of fruits and vegetables using YOLOv4-tiny proposed by Bochkovskiy et al. (2020) ; Latha et al. (2022) achieved a mean AP of 51% and a speed of 55.6 fps.…”

Section: Introductionmentioning

confidence: 99%

An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting

2023

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An improved YOLOv5s model was proposed and validated on a new fruit dataset to solve the real-time detection task in a complex environment. With the incorporation of feature concatenation and an attention mechanism into the original YOLOv5s network, the improved YOLOv5s recorded 122 layers, 4.4 × 106 params, 12.8 GFLOPs, and 8.8 MB weight size, which are 45.5%, 30.2%, 14.1%, and 31.3% smaller than the original YOLOv5s, respectively. Meanwhile, the obtained 93.4% of mAP tested on the valid set, 96.0% of mAP tested on the test set, and 74 fps of speed tested on videos using improved YOLOv5s is 0.6%, 0.5%, and 10.4% higher than the original YOLOv5s model, respectively. Using videos, the fruit tracking and counting tested on the improved YOLOv5s observed less missed and incorrect detections compared to the original YOLOv5s. Furthermore, the aggregated detection performance of improved YOLOv5s outperformed the network of GhostYOLOv5s, YOLOv4-tiny, and YOLOv7-tiny, including other mainstream YOLO variants. Therefore, the improved YOLOv5s is lightweight with reduced computation costs, can better generalize against complex conditions, and is applicable for real-time detection in fruit picking robots and low-power devices.

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Fruits and Vegetables Recognition using YOLO

Cited by 17 publications

References 14 publications

Fruit ripeness identification using YOLOv8 model

Fruit ripeness identification using YOLOv8 model

An FPGA-Based Hardware Low-Cost, Low-Consumption Target-Recognition and Sorting System

An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting

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