Index Networks

Lu, Hao; Dai, Yutong; Shen, Chunhua; Xu, Songcen

doi:10.1109/tpami.2020.3004474

Cited by 38 publications

(47 citation statements)

References 44 publications

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“…DCT Network Architecture. Practically, DCT can be easily implemented as a dynamic network [21,22]. Since off-the-shelf networks exhibit superior performance on computer vision tasks, in this work, we evaluate four different network architectures: ShuffleNetV2 [23], MobileNetV2 [43], ResNet18 [16], and ResNet34 [16].…”

Section: Predicting α and βmentioning

confidence: 99%

“…The commonly used attention mechanisms include channel-wise attention [ 40 ], spatial-wise attention [ 41 ], or both [ 42 ]. Akin to soft attention, IndexNet [ 21 , 22 ] is proposed to deal with the downsampling/upsampling stage in deep networks.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, we present an analysis of the impact of the color channel and propose to deal with colors with dynamic color transform (DCT). The DCT is in the same spirit of recent dynamic networks [ 21 , 22 ] that enable date-dependent inference. For example, the DCT follows a linear color model that dynamically generates 2 parameters to modulate the color of the input image.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Color Transform Networks for Wheat Head Detection

2022

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Wheat head detection can measure wheat traits such as head density and head characteristics. Standard wheat breeding largely relies on manual observation to detect wheat heads, yielding a tedious and inefficient procedure. The emergence of affordable camera platforms provides opportunities for deploying computer vision (CV) algorithms in wheat head detection, enabling automated measurements of wheat traits. Accurate wheat head detection, however, is challenging due to the variability of observation circumstances and the uncertainty of wheat head appearances. In this work, we propose a simple but effective idea—dynamic color transform (DCT)—for accurate wheat head detection. This idea is based on an observation that modifying the color channel of an input image can significantly alleviate false negatives and therefore improve detection results. DCT follows a linear color transform and can be easily implemented as a dynamic network. A key property of DCT is that the transform parameters are data-dependent such that illumination variations can be corrected adaptively. The DCT network can be incorporated into any existing object detectors. Experimental results on the Global Wheat Detection Dataset (GWHD) 2021 show that DCT can achieve notable improvements with negligible overhead parameters. In addition, DCT plays an important role in our solution participating in the Global Wheat Challenge (GWC) 2021, where our solution ranks the first on the initial public leaderboard, with an Average Domain Accuracy (ADA) of 0.821, and obtains the runner-up reward on the final private testing set, with an ADA of 0.695.

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Section: Predicting α and βmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Color Transform Networks for Wheat Head Detection

2022

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“…The idea of counting by regression is further amplified by Lempitsky and Zisserman ( 2010 ) who introduce the concept of the density map. The density map is generated from dotted annotations with Gaussian smoothing such that each pixel is assigned with a value that corresponds to the object density, which transforms counting into a dense prediction problem (Lu et al, 2019 , 2020 ). It has become the basic building block for many object counting models (Chen et al, 2013 ; Arteta et al, 2014 ) including recent deep counting networks (Zhang et al, 2015 , 2016 ; Sindagi and Patel, 2017 ; Li et al, 2018 ; Liu et al, 2020 ; Ma et al, 2019 ; Xiong et al, 2019b ).…”

Section: Introductionmentioning

confidence: 99%

TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery

Lü

2020

Front. Plant Sci.

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Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.

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“…Targeting in-field maize plants, a representative agricultural crop, the goal of this work is to present a comprehensive evaluation of state-of-the-art object detection and object counting methods on the task of maize tassels counting. Object detection is a typical dense prediction problem [29,30]. In recent years, there appear many advanced object detection approaches, such as R-CNN [21], Fast R-CNN [31], Faster R-CNN [22], SSD [32], YOLO9000 [33], RetinaNet [34], etc.…”

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confidence: 99%

Maize tassels detection: a benchmark of the state of the art

Zou

et al. 2020

Plant Methods

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Background: The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection-and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. Results: Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels-TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection-and regression-based counting approaches to benefit nextgeneration intelligent agriculture. Conclusions: Current state-of-the-art detection-and regression-based counting approaches can all achieve a relatively high degree of accuracy when dealing with in-field maize tassels, with at least 0.85 R 2 values and 28.2% rRMSE error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https ://git.io/MTDC

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Index Networks

Cited by 38 publications

References 44 publications

Dynamic Color Transform Networks for Wheat Head Detection

Dynamic Color Transform Networks for Wheat Head Detection

TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery

Maize tassels detection: a benchmark of the state of the art

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