Towards Tobacco Leaf Detection Using Haar Cascade Classifier and Image Processing Techniques

Marzan, Charlie S.; Marcos, Nelson

doi:10.1145/3282286.3282292

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Gaussian blurring [33], and by morphological operations [34]. between -3 and 3 times the value of the standard deviation of the pixels, thus avoiding the tendency to infinity towards the negative side of the plane [35].…”

Section: Source: Own Workmentioning

confidence: 99%

Background subtraction and yolo algorithm

Rondón¹,

Casadiego²,

Delgado³

2021

ing. Solidar

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Introduction: This article is the result of research entitled “Signal processing system for the detection of people in agglomerations in areas of public space in the city of Cúcuta”, developed at the Universidad Francisco de Paula Santander in 2020.Problem: The high percentage of false positives and false negatives in people detection processes makes decision making in video surveillance, tracking and tracing applications complex. Objective: To determine which technique for the detection of people presents better results in terms of respon-se time and detection hits.Methodology: Two techniques for the detection of people in uncontrolled environments are validated in Python with videos taken inside the Universidad Francisco de Paula Santander: Background subtraction and the YOLO algorithm.Results: With the background subtraction technique, we obtained a hit rate of 84.07 % and an average response time of 0.815 seconds. Likewise, with the YOLO algorithm the hit rate and average response time are 90% and 4.59 seconds respectively.Conclusion: It is possible to infer the use of the background subtraction technique in hardware tools such as the Pi 3B+ Raspberry board for processes in which the analysis of information in real time is prioritized, while the YOLO algorithm presents the characteristics required in the processes in which the information is analyzed after the acquisition of the image.Originality: Through this research, aspects required for the real-time analysis of information obtained in pro-cesses of people detection in uncontrolled environments were analyzed. Limitations: The analyzed videos were taken only at the Universidad Francisco de Paula Santander. Also, the Raspberry Pi 3B+ board overheats when processing the video images, due to the full resource requirement of the device.

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Section: Source: Own Workmentioning

confidence: 99%

Background subtraction and yolo algorithm

Rondón¹,

Casadiego²,

Delgado³

2021

ing. Solidar

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show abstract

“…Los filtros de suavizado son conocidos también como filtros de desenfoque gaussiano. En estos las distribuciones con desplazamientos en el eje x se obtienen como se presenta en la ecuación 3, en la que se hace necesaria una operación de truncamiento para el valor de x, que debe estar comprendido en el rango positivo y negativo de tres veces el valor de la varianza de los pixeles (Marzan & Marcos, 2018).…”

Section: Filtrados De Suavizado Y Morfologíaunclassified

Procesamiento a imágenes de video para verificación de distanciamiento social durante la pandemia de la COVID-19

Rondón¹,

Casadiego²,

Delgado³

et al. 2020

RLCT

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El aumento exponencial del número de contagios por COVID-19 alrededor del mundo ha originado que los gobiernos, en acompañamiento de comunidades médicas y científicas, recomienden el distanciamiento social de al menos dos metros entre personas, para evitar el crecimiento en el número de contagios. Con el propósito de determinar el cumplimiento de la medida de distanciamiento social, se realiza una prueba piloto mediante el procesamiento a imágenes de video para la detección de personas en espacios abiertos de la zona céntrica de la ciudad de Cúcuta, Colombia. El método propuesto consiste en el análisis de información de videos de personas con fondo estático, tomados desde una altura de 4.5 metros, además de ubicación de centroides por el método de momentos y estimación de distancia por el método euclidiano, esto, mediante lenguaje de programación Python. Como resultado se obtiene un acierto en la detección de personas del 92.43 % y que el 84.21 % de quienes circularon por el cuadro de video y transitan en la zona céntrica de la ciudad de Cúcuta, Colombia, incumplen con el distanciamiento social de dos metros recomendado por la comunidad científica y expuesta en la circular municipal 023 de 2020.

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“…There are many platforms that exist today for collecting and acquiring images quickly and cheaply such as remote sensing very high-resolution satellites [8], world view-2, GeoEye-1 [9], and unmanned aerial vehicle (UAV). The remote sensing images and videos are used for many applications for object detection and tracking, identifying and detecting buildings, offices, and also shapes of the building [10].…”

Section: Introductionmentioning

confidence: 99%

Cluster-based segmentation for tobacco plant detection and classification

Thimmegowda¹,

Chandrika²

2023

Bulletin EEI

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Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.

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Towards Tobacco Leaf Detection Using Haar Cascade Classifier and Image Processing Techniques

Cited by 4 publications

References 12 publications

Background subtraction and yolo algorithm

Background subtraction and yolo algorithm

Procesamiento a imágenes de video para verificación de distanciamiento social durante la pandemia de la COVID-19

Cluster-based segmentation for tobacco plant detection and classification

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