Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

Zhi-bin, Cheng; Zhang, Fuquan

doi:10.1155/2020/8870649

Cited by 30 publications

(7 citation statements)

References 26 publications

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“…Pada bagian neck, YOLOv5 menggunakan Path Aggregation Network (PANet) sebagai mekanisme polimerasi parametrik untuk berbagai tingkat detektor (Liu, dkk., 2020). Kisi fitur terhubung ke semua lapisan fitur oleh kumpulan fitur adaptif yang disediakan PANet (Cheng & Zhang, 2020).…”

Section: Yolov5unclassified

Sistem Inspeksi Cacat pada Permukaan Kayu menggunakan Model Deteksi Obyek YOLOv5

Akhyar

NOVAMIZANTI²,

RIANTIARNI³

2022

ELKOMIKA

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ABSTRAKPermukaan kayu mengalami berbagai serangan serangga dan jamur, sehingga dapat menyebabkan cacat seperti pembusukan pada kayu yang berpengaruh terhadap kualitas dan harga jual kayu tersebut. Pengujian secara lapangan dengan penglihatan manusia menjadi kurang efektif, karena menghasilkan penilaian yang subjektif dan memerlukan waktu yang lama. Penelitian ini mengusulkan sistem deteksi cacat pada permukaan kayu pinus dan kayu karet menggunakan Convolutional Neural Network (CNN) dengan model YOLOv5. Pengujian sistem dilakukan menggunakan beberapa model dari YOLOv5, serta dua teknik image enhancement, yaitu edge filter dan Real ESRGAN. Hasil mAP terbaik sebesar 94,3% dengan kecepatan 125 FPS pada dataset kayu pinus menggunakan model YOLOv5s tanpa penambahan image enhancement. Sedangkan pada dataset kayu karet yang memiliki jenis cacat yang lebih kompleks, hasil mAP terbaik adalah sebesar 94,7% dengan kecepatan 139 FPS menggunakan model YOLOv5s-Transformer dengan penambahan image enhancement Real ESRGAN.Kata kunci: deteksi, kayu, Convolutional Neural Network (CNN), YOLO ABSTRACTWood surafce is subject to various insect and fungal attacks, which can cause defects such as wood rot. This condition affects the quality as well as the selling price of the wood. Field testing with human eyesight becomes less effective because it produces a subjective assessment and time consuming. This study proposes a defect detection system on the surface of pine wood and rubber wood using the Convolutional Neural Network (CNN) with the YOLOv5 model. System testing was carried out using several models from YOLOv5 and two image enhancement techniques, namely edge filter and Real ESRGAN. The best mAP results were 94.3%, with a speed of 125 FPS on the pine wood dataset using the YOLOv5s model without adding image enhancement. While on the rubber wood dataset with more complex defect problem, the best mAP results were 94.7% with a speed of 139 FPS using the YOLOv5s-Transformer model with the addition of image enhancement Real ESRGAN.Keywords: detection, wood, Convolutional Neural Network (CNN), YOLO

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Section: Yolov5unclassified

Sistem Inspeksi Cacat pada Permukaan Kayu menggunakan Model Deteksi Obyek YOLOv5

Akhyar

NOVAMIZANTI²,

RIANTIARNI³

2022

ELKOMIKA

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“…In 2016, Redmon published a paper on Unified Real-Time Object Detection (YOLO) [32,33], which presented a simple convolutional neural network approach with good results and speed, and was the first implementation of realtime object detection. The task of the RPN is to output objects based on their attribute scores and then classify them using RoI pooling and fully connected layers.…”

Section: Artificial Intelligence For Image Detectionmentioning

confidence: 99%

AI-Based W-Band Suspicious Object Detection System for Moving Persons: Two-Stage Walkthrough Configuration and Recognition Optimization

Wen

et al. 2022

Wireless Communications and Mobile Computing

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In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners’ ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.

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“…The feature grid is connected to all the feature layers by the adaptive feature pools provided by PANet. Consequently, the useful information obtained from each feature layer can be transmitted directly to the proposed subnetwork (Liu et al 2018;Cheng and Zhang 2020).…”

Section: Architecture Of Yolo-v5 Modelmentioning

confidence: 99%

Accurate and automated detection of surface knots on sawn timbers using YOLO-V5 model

Fang

Guo

Chen

et al. 2021

BioRes

120

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Knot detection is a challenging problem for the wood industry. Traditional methodologies depend heavily on the features selected manually and therefore were not always accurate due to the variety of knot appearances. This paper proposes an automated framework for addressing the aforementioned problem by using the state-of-the-art YOLO-v5 (the fifth version of You Only Look Once) detector. The features of surface knots were learned and extracted adaptively, and then the knot defects were identified accurately even though the knots vary in terms of color and texture. The proposed method was compared with YOLO-v3 SPP and Faster R-CNN on two datasets. Experimental results demonstrated that YOLO-v5 model achieved the best performance for detecting surface knot defects. F-Score on Dataset 1 was 91.7% and that of Dataset 2 was up to 97.7%. Moreover, YOLO-v5 has clear advantages in terms of training speed and the size of the weight file. These advantages made YOLO-v5 more suitable for the detection of surface knots on sawn timbers and potential for timber grading.

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Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

Cited by 30 publications

References 26 publications

Sistem Inspeksi Cacat pada Permukaan Kayu menggunakan Model Deteksi Obyek YOLOv5

Sistem Inspeksi Cacat pada Permukaan Kayu menggunakan Model Deteksi Obyek YOLOv5

AI-Based W-Band Suspicious Object Detection System for Moving Persons: Two-Stage Walkthrough Configuration and Recognition Optimization

Accurate and automated detection of surface knots on sawn timbers using YOLO-V5 model

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