Line detection via a lightweight CNN with a Hough layer

Teplyakov, Lev; Kaymakov, Kirill; Shvets, Evgeny; Nikolaev, Dmitry P.

doi:10.1117/12.2587167

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…In addition, the linear Hough transform can be utilized for detecting or analyzing arbitrary (non-parametric) curves by examining the shape of peaks or their locations in the parameter space [74]. Teplyakov et al [75] proposed a lightweight Artificial Neural Network for line detection with several convolutional layers and a fast Hough transform layer that can be trained in an end-to-end manner. They proposed to use fast Hough transform (FHT) with O(N2logN) complexity.…”

Section: Spatial Context Featurementioning

confidence: 99%

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

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Section: Spatial Context Featurementioning

confidence: 99%

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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“…Each of the listed approaches can be implemented using both low-parameter methods [55] and machine [50] methods, including deep learning [46,58]. There are also methods that combine neural networks and classical computer vision approaches [59,60].…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2021

Preprint

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This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

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“…Каждый из перечисленных подходов может быть реализован как в виде малопараметрического метода [32], так и в виде метода машинного обучения [27], включая глубокое обучение [22,35,36]. Существуют также методы, сочетающие нейронные сети и классические подходы компьютерного зрения [37,38].…”

Section: Introductionunclassified

Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2023

Computer Optics

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This paper considers an issue of delineating agricultural fields in satellite images. In this task we follow a multi-temporal data approach. We show that on such data, good quality can be achieved using a simple low-parameter method. The method consists of a combination of a field detector and an edge detector. The field detection is based on an Otsu thresholding technique and for the edge detection we use a Canny detector. Facing a lack of available datasets and aiming to estimate the proposed algorithm, we prepared and published our dataset consisting of 18,859 expertly annotated fields using Sentinel-2 data. We implement one of the state-of-the-art deep-learning approaches and compare it with the proposed method on our dataset. The experiment shows the proposed simple multi-temporal algorithm to outperform the state-of-the-art instant data approach. This result confirms the importance of using multi-temporal data and for the first time demonstrates that the delineation problem can be solved at a lower cost without loss of quality. Our approach requires a significantly less amount of training data when compared with the NN-based one. The dataset of agricultural fields used in the work and the proposed algorithm implementation in Python are published in open access.

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Line detection via a lightweight CNN with a Hough layer

Cited by 9 publications

References 24 publications

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data

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