Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method

Author(s):  
Dan Jeric Arcega Rustia ◽  
Jun‐Jee Chao ◽  
Lin‐Ya Chiu ◽  
Ya‐Fang Wu ◽  
Jui‐Yung Chung ◽  
...  
2021 ◽  
pp. 1-14
Author(s):  
Waqas Yousaf ◽  
Arif Umar ◽  
Syed Hamad Shirazi ◽  
Zakir Khan ◽  
Imran Razzak ◽  
...  

Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work.


2021 ◽  
Author(s):  
Wenfeng Li ◽  
Yuewu Yang ◽  
Liwei Zhang ◽  
Xiaochen Xu ◽  
Haobo Ma ◽  
...  

2018 ◽  
Vol 124 (11) ◽  
Author(s):  
Benlan Shen ◽  
Jun Chang ◽  
Chuhan Wu ◽  
Yihan Jin ◽  
Weilin Chen ◽  
...  

2019 ◽  
Vol 94 ◽  
pp. 524-535 ◽  
Author(s):  
Ningbo Liu ◽  
Yanan Xu ◽  
Yonghua Tian ◽  
Hongwei Ma ◽  
Shuliang Wen

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.


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