A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5

Jia, Yinhua; Qi, Jiaming; Zhang, Jie; Shao, Hongmin; Yang, Jia; Li, Xin

doi:10.3390/electronics10141711

Cited by 208 publications

(97 citation statements)

References 13 publications

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“…We evaluated the performance of the recognition models based on the mAP value. The higher the value, the better the average detection accuracy [40]. Tables 3 and 4 compare the mAP values and inference times of four different YOLOv5 models with pre-trained weights, respectively.…”

Section: Comparison and Resultsmentioning

confidence: 99%

“…Bochkovskiy et al [25] proposed YOLOv4, consisting of CSPDarknet53 as a backbone network and spatial pyra-mid pooling (SPP), with PANet as the neck part and YOLOv3 as the head part. YOLOv5 is the latest YOLO version that uses the CBL (Conv2D + Batch Normal + LeakyRELU) module as the basic convolution module and the BottleneckCSP module for feature extraction [39,40]. YOLOv5 includes different models, such as YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, which differ by the width and depth of the BottleneckCSP module [41].…”

Section: Yolo Algorithmmentioning

confidence: 99%

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Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Liau

Ryu

2021

Sustainability

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Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time.

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Section: Comparison and Resultsmentioning

confidence: 99%

Section: Yolo Algorithmmentioning

confidence: 99%

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Liau

Ryu

2021

Sustainability

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“…At the same time, it has inherited the YOLO series, which is easy to deploy, and has made some empirical improvements to construct a new high-performance detector. When choosing the benchmark model of YOLOX, the authors believes that the Yolov4 [ 16 ] and Yolov5 [ 17 ] series may have some over-optimization from the perspective of the algorithm based on the anchor frame; hence, they finally chose Yolov3 [ 18 ] and combined it with the SPP [ 19 ] components to develop the Yolov3_spp version with better performance. Based on this, the authors proposed the network structure of YOLOX-Darknet53, as shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

Jia

Wang

Xiang

et al. 2022

Plants

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The prevention and management of crop diseases play an important role in agricultural production, but there are many types of crop diseases and complex causes, and their prevention and identification add difficulties to the process. The traditional methods of identifying diseases mostly rely on human visual and manual inspection, which requires a certain amount of expert knowledge and experience. There are shortcomings such as strong subjectivity and low accuracy. This paper takes the common diseases of kiwifruit as the research object. Based on deep learning and computer vision models, and given the influence of a complex background in actual scenes on the detection of diseases, as well as the shape and size characteristics of diseases, an innovative method of target detection and semantic segmentation was proposed to identify diseases accurately. The main contributions of this research are as follows: We produced the world’s first high-quality dataset on kiwifruit. We used the target detection algorithm YOLOX, we stripped the kiwi leaves from the natural background and removed the influencing factors existing in the complex background. Based on the mainstream semantic segmentation networks UNet and DeepLabv3+, the experimental results showed that the ResNet101 network achieved the most effective results in the identification of kiwi diseases, with an accuracy rate of 96.6%. We used the training method of learning rate decay to further improve the training effect without increasing the training cost. After experimental verification, our two-stage disease detection algorithm had the advantages of high accuracy, strong robustness, and wide detection range, which provided a more efficient solution for solving the problem of precise monitoring of crop growth environment parameters.

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“…Presently, the majority of researchers focus on improving the static detection effect of different fruit targets [20][21][22]. However, related studies on the dynamic tracking and accurate counting of green citrus have seen less attention.…”

Section: Introductionmentioning

confidence: 99%

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

Lyu

Zhao

et al. 2022

Sensors

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Green citrus detection in citrus orchards provides reliable support for production management chains, such as fruit thinning, sunburn prevention and yield estimation. In this paper, we proposed a lightweight object detection YOLOv5-CS (Citrus Sort) model to realize object detection and the accurate counting of green citrus in the natural environment. First, we employ image rotation codes to improve the generalization ability of the model. Second, in the backbone, a convolutional layer is replaced by a convolutional block attention module, and a detection layer is embedded to improve the detection accuracy of the little citrus. Third, both the loss function CIoU (Complete Intersection over Union) and cosine annealing algorithm are used to get the better training effect of the model. Finally, our model is migrated and deployed to the AI (Artificial Intelligence) edge system. Furthermore, we apply the scene segmentation method using the “virtual region” to achieve accurate counting of the green citrus, thereby forming an embedded system of green citrus counting by edge computing. The results show that the mAP@.5 of the YOLOv5-CS model for green citrus was 98.23%, and the recall is 97.66%. The inference speed of YOLOv5-CS detecting a picture on the server is 0.017 s, and the inference speed on Nvidia Jetson Xavier NX is 0.037 s. The detection and counting frame rate of the AI edge system-side counting system is 28 FPS, which meets the counting requirements of green citrus.

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A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5

Cited by 208 publications

References 13 publications

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

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