VFNet: A Convolutional Architecture for Accent Classification

Ahmed, Asad; Tangri, Pratham; Panda, Anirban; Ramani, Dhruv; Karmakar, Samarjit

doi:10.48550/arxiv.1910.06697

Cited by 1 publication

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“…The object detection architectures investigated in the present study included CenterNet, 34 Faster R-CNN, 35 Trident Network (TridentNet), 36 Variable Filter Net (VFNet), 37 and You Only Look Once (YOLO) version 3 (YOLOv3), 38 CenterNet utilizes keypoint estimation to identify center points and meanwhile regresses to all other object properties, including three-dimensional (3D) location, even pose, orientation, and size. 34 Faster R-CNN shares the convolutional features of Region Proposal Network and Fast R-CNN and thus enables a near real-time frame detection speed.…”

Section: Object Detectionmentioning

confidence: 99%

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Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

Zhuang

Bagavathiannan

et al. 2021

Pest Management Science

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BACKGROUND In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

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Section: Object Detectionmentioning

confidence: 99%

“…36 VFNet captures a hierarchy of features through the application of variable filter sizes along with the audio spectrograms. 37 VFNet was originally designed for accent classification 37 but its feasibility for weed detection was evaluated in the present study. YOLOv3 was developed based on YOLO 39 and YOLO version 2.…”

Section: Object Detectionmentioning

confidence: 99%

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

Zhuang

Bagavathiannan

et al. 2021

Pest Management Science

View full text Add to dashboard Cite

show abstract

VFNet: A Convolutional Architecture for Accent Classification

Cited by 1 publication

References 0 publications

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

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