YOLOv5-LiNet: A lightweight network for fruits instance segmentation

Lawal, Olarewaju Mubashiru

doi:10.1371/journal.pone.0282297

Cited by 25 publications

(3 citation statements)

References 30 publications

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“…On Tesla V100, YOLOv5 achieves real-time detection speeds of 156 FPS on the COCO2017 dataset with an accuracy of 56.8% AP. In recent years, YOLOv5 has been widely applied in various fields such as industry [30,31], agriculture [32,33], etc. The structure of YOLOv5 mainly consists of four parts.…”

Section: Yolov5mentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model’s adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model’s mAP50 still exceeds 70%, demonstrating the model’s strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency.

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Section: Yolov5mentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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“…On the Tesla V100, the real-time detection speed of the COCO2017 dataset reaches 156 FPS, and the accuracy rate is 56.8% AP. At present, YOLOV5 is widely used in many different application scenarios, such as agriculture [ 21 , 22 ], industry [ 23 , 24 ] and other industries. In this paper, YOLOV5s is selected as the basic algorithm, taking into account the balance between the target detection accuracy and speed.…”

Section: Related Workmentioning

confidence: 99%

Strip Surface Defect Detection Algorithm Based on YOLOv5

et al. 2023

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In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.

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“…Sozzi et al (2022) tested six versions of the original YOLO model, and the results demonstrated that YOLOv5s can identify green grapes quickly and accurately. Lawal (2023) upgraded the YOLOv5 backbone and neck networks and changed the loss function to EIoU to improve the robustness in complicated and ever-changing situations. Lawal (2021) improved the YOLOv3 model to solve interference problems such as branch and leaf obstruction, lighting shifts, and fruit overlapping.…”

Section: Introductionmentioning

confidence: 99%

Classification of peanut pod rot based on improved YOLOv5s

Liu,

Li,

Fan

et al. 2024

Front. Plant Sci.

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Peanut pod rot is one of the major plant diseases affecting peanut production and quality over China, which causes large productivity losses and is challenging to control. To improve the disease resistance of peanuts, breeding is one significant strategy. Crucial preventative and management measures include grading peanut pod rot and screening high-contributed genes that are highly resistant to pod rot should be carried out. A machine vision-based grading approach for individual cases of peanut pod rot was proposed in this study, which avoids time-consuming, labor-intensive, and inaccurate manual categorization and provides dependable technical assistance for breeding studies and peanut pod rot resistance. The Shuffle Attention module has been added to the YOLOv5s (You Only Look Once version 5 small) feature extraction backbone network to overcome occlusion, overlap, and adhesions in complex backgrounds. Additionally, to reduce missing and false identification of peanut pods, the loss function CIoU (Complete Intersection over Union) was replaced with EIoU (Enhanced Intersection over Union). The recognition results can be further improved by introducing grade classification module, which can read the information from the identified RGB images and output data like numbers of non-rotted and rotten peanut pods, the rotten pod rate, and the pod rot grade. The Precision value of the improved YOLOv5s reached 93.8%, which was 7.8%, 8.4%, and 7.3% higher than YOLOv5s, YOLOv8n, and YOLOv8s, respectively; the mAP (mean Average Precision) value was 92.4%, which increased by 6.7%, 7.7%, and 6.5%, respectively. Improved YOLOv5s has an average improvement of 6.26% over YOLOv5s in terms of recognition accuracy: that was 95.7% for non-rotted peanut pods and 90.8% for rotten peanut pods. This article presented a machine vision- based grade classification method for peanut pod rot, which offered technological guidance for selecting high-quality cultivars with high resistance to pod rot in peanut.

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YOLOv5-LiNet: A lightweight network for fruits instance segmentation

Cited by 25 publications

References 30 publications

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Strip Surface Defect Detection Algorithm Based on YOLOv5

Classification of peanut pod rot based on improved YOLOv5s

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