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Jesus Ruiz-Santaquiteria
Department of Electrical, Electronic, Automatic and Communications Engineering—IEEAC, Higher Technical School of Industrial Engineering, University of Castilla-La Mancha, Avenida de Camilo José Cela s/n, 13071 Ciudad Real, Spain

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Journal article
Published: 30 June 2021 in Applied Sciences
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There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

ACS Style

Jesus Salido; Vanesa Lomas; Jesus Ruiz-Santaquiteria; Oscar Deniz. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences 2021, 11, 6085 .

AMA Style

Jesus Salido, Vanesa Lomas, Jesus Ruiz-Santaquiteria, Oscar Deniz. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences. 2021; 11 (13):6085.

Chicago/Turabian Style

Jesus Salido; Vanesa Lomas; Jesus Ruiz-Santaquiteria; Oscar Deniz. 2021. "Automatic Handgun Detection with Deep Learning in Video Surveillance Images." Applied Sciences 11, no. 13: 6085.

Journal article
Published: 31 August 2020 in Applied Sciences
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Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.

ACS Style

Jesús Salido; Carlos Sánchez; Jesús Ruiz-Santaquiteria; Gabriel Cristóbal; Saul Blanco; Gloria Bueno. A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms. Applied Sciences 2020, 10, 6033 .

AMA Style

Jesús Salido, Carlos Sánchez, Jesús Ruiz-Santaquiteria, Gabriel Cristóbal, Saul Blanco, Gloria Bueno. A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms. Applied Sciences. 2020; 10 (17):6033.

Chicago/Turabian Style

Jesús Salido; Carlos Sánchez; Jesús Ruiz-Santaquiteria; Gabriel Cristóbal; Saul Blanco; Gloria Bueno. 2020. "A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms." Applied Sciences 10, no. 17: 6033.

Journal article
Published: 25 July 2017 in Applied Sciences
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This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.

ACS Style

Gloria Bueno; Oscar Deniz; Anibal Pedraza; Jesús Ruiz-Santaquiteria; Jesús Salido; Gabriel Cristóbal; María Borrego-Ramos; Saúl Blanco. Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Applied Sciences 2017, 7, 753 .

AMA Style

Gloria Bueno, Oscar Deniz, Anibal Pedraza, Jesús Ruiz-Santaquiteria, Jesús Salido, Gabriel Cristóbal, María Borrego-Ramos, Saúl Blanco. Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Applied Sciences. 2017; 7 (8):753.

Chicago/Turabian Style

Gloria Bueno; Oscar Deniz; Anibal Pedraza; Jesús Ruiz-Santaquiteria; Jesús Salido; Gabriel Cristóbal; María Borrego-Ramos; Saúl Blanco. 2017. "Automated Diatom Classification (Part A): Handcrafted Feature Approaches." Applied Sciences 7, no. 8: 753.