Recognition and Detection of Greenhouse Tomatoes in Complex Environment

Gao, Guohua; Wang, Shuangyou; Shuai, Ciyin; Zhang, Zihua; Zhang, Shuo; Feng, Yongbing

doi:10.18280/ts.390130

Cited by 16 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of them is represented in the following manner: <object-class-ID> <X center> <Y center> <Box width> <Box height>. The Equations ( 1)-( 6) serve as the foundation for the adjustment procedure [50].…”

Section: Compared Methodsmentioning

confidence: 99%

Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

Dewi

Chen

Zhuang

et al. 2023

BDCC

View full text Add to dashboard Cite

In recent years, there have been significant advances in deep learning and road marking recognition due to machine learning and artificial intelligence. Despite significant progress, it often relies heavily on unrepresentative datasets and limited situations. Drivers and advanced driver assistance systems rely on road markings to help them better understand their environment on the street. Road markings are signs and texts painted on the road surface, including directional arrows, pedestrian crossings, speed limit signs, zebra crossings, and other equivalent signs and texts. Pavement markings are also known as road markings. Our experiments briefly discuss convolutional neural network (CNN)-based object detection algorithms, specifically for Yolo V2, Yolo V3, Yolo V4, and Yolo V4-tiny. In our experiments, we built the Taiwan Road Marking Sign Dataset (TRMSD) and made it a public dataset so other researchers could use it. Further, we train the model to distinguish left and right objects into separate classes. Furthermore, Yolo V4 and Yolo V4-tiny results can benefit from the “No Flip” setting. In our case, we want the model to distinguish left and right objects into separate classes. The best model in the experiment is Yolo V4 (No Flip), with a test accuracy of 95.43% and an IoU of 66.12%. In this study, Yolo V4 (without flipping) outperforms state-of-the-art schemes, achieving 81.22% training accuracy and 95.34% testing accuracy on the TRMSD dataset.

show abstract

Section: Compared Methodsmentioning

confidence: 99%

Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

Dewi

Chen

Zhuang

et al. 2023

BDCC

View full text Add to dashboard Cite

show abstract

“…The category "Other Fruits" corresponds to fruits that are analyzed in a single article, usually fruits of local origin. Agrivita 1 [115] Applied Intelligence 1 [116] Computational Intelligence and Neuroscience 1 [117] Computer Systems Science and Engineering 1 [118] Computers Electrical Engineering 1 [119] Data in Brief 1 [42] Ecological Informatics 1 [120] Electronics 1 [121] Energy Reports 1 [122] European Food Research and Technology 1 [123] Expert Systems With Applications 1 [124] Frontiers in Robotics and AI 1 [125] Horticulturae 1 [126] Journal of Robotics and Mechatronics 1 [135] Jurnal Kejuruteraan 1 [136] Mathematical Problems in Engineering 1 [137] Continued on next page Mechanical Systems and Signal Processing 1 [139] Multimedia Systems 1 [140] Neural Computing Applications 1 [141] Neural Network World 1 [142] Neural Networks 1 [143] Neurocomputing 1 [144] Plant and Cell Physiology 1 [145] Plants 1 [146] Postharvest Biology and Technology 1 [147] Procedia Computer Science 1 [148] Remote Sensing 1 [149] Results in Engineering 1 [150] Scientia Horticulturae 1 [151] Scientific African 1 [152] Scientific Programming 1 [153] Scientific Reports 1 [154] Sn Applied Sciences 1 [50] Sustainability 1 [155] Traitement du Signal 1 [156] Visual Computer 1 [52]…”

Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

show abstract

“…In addition, SPP has no shortage of improvements to it. Guohua Gao et al [51] pointed out that the concatenation of max-pooling output results in the SPP module will reduce the resolution of the image and easily lose local information. Therefore, two dilated convolutional layers are added to the original module, which expands the space size and helps to capture multiscale global information at different sampling rates.…”

Section: Improvements Made To the Spp Modulementioning

confidence: 99%

Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5

Luo

Wang

2022

Remote Sensing

View full text Add to dashboard Cite

Due to the advantages of small size, lightweight, and simple operation, the unmanned aerial vehicle (UAV) has been widely used, and it is also becoming increasingly convenient to capture high-resolution aerial images in a variety of environments. Existing target-detection methods for UAV aerial images lack outstanding performance in the face of challenges such as small targets, dense arrangement, sparse distribution, and a complex background. In response to the above problems, some improvements on the basis of YOLOv5l have been made by us. Specifically, three feature-extraction modules are proposed, using asymmetric convolutions. They are named the Asymmetric ResNet (ASResNet) module, Asymmetric Enhanced Feature Extraction (AEFE) module, and Asymmetric Res2Net (ASRes2Net) module, respectively. According to the respective characteristics of the above three modules, the residual blocks in different positions in the backbone of YOLOv5 were replaced accordingly. An Improved Efficient Channel Attention (IECA) module was added after Focus, and Group Spatial Pyramid Pooling (GSPP) was used to replace the Spatial Pyramid Pooling (SPP) module. In addition, the K-Means++ algorithm was used to obtain more accurate anchor boxes, and the new EIOU-NMS method was used to improve the postprocessing ability of the model. Finally, ablation experiments, comparative experiments, and visualization of results were performed on five datasets, namely CIFAR-10, PASCAL VOC, VEDAI, VisDrone 2019, and Forklift. The effectiveness of the improved strategies and the superiority of the proposed method (YOLO-UAV) were verified. Compared with YOLOv5l, the backbone of the proposed method increased the top-one accuracy of the classification task by 7.20% on the CIFAR-10 dataset. The mean average precision (mAP) of the proposed method on the four object-detection datasets was improved by 5.39%, 5.79%, 4.46%, and 8.90%, respectively.

show abstract

Recognition and Detection of Greenhouse Tomatoes in Complex Environment

Cited by 16 publications

References 0 publications

Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5

Contact Info

Product

Resources

About