Object detection using deep neural networks

Shah, Malay B.; Kapdi, Rupal

doi:10.1109/iccons.2017.8250570

Cited by 27 publications

(7 citation statements)

References 2 publications

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“…However, these hand-crafted methods remained susceptible to occlusion and other complex environments, not suitable for real-time scenarios. However, recent deep learning methods allow the network to produce high-level features of objects with real-time processing speed [15,32].…”

Section: Pedestrian and Cyclist Detectionmentioning

confidence: 99%

Flexible neural network for fast and accurate road scene perception

Mehtab

Yan

2022

Multimed Tools Appl

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Accurate object detection on the road is the most important requirement of autonomous vehicles. Extensive work has been accomplished for car, pedestrian, and cyclist detection; however, comparatively, very few efforts have been put into 2D object detection. In this article, a dynamic approach is investigated to design a perfect unified neural network that could achieve the best results based on our available hardware. The proposed architecture is based on CSPNet for feature extraction in an end-to-end way. The net extracts visual features by using backbone subnet, visual object detection is based on a feature pyramid network (FPN). In order to increase the net flexibility, an auto-anchor generating method is applied to the detection layer that makes the net suitable for any datasets. For fine-tuning the net, activation, optimization, and loss functions are considered along with multiple check points. The proposed net is trained and tested based on the benchmark KITTI dataset. Our extensive experiments show that the proposed model for visual object detection is superior to others, where other nets output very low accuracy for pedestrian and cyclist detection, our proposed model achieves 99.3% recall rate based on our dataset.

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Section: Pedestrian and Cyclist Detectionmentioning

confidence: 99%

Flexible neural network for fast and accurate road scene perception

Mehtab

Yan

2022

Multimed Tools Appl

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“…new Object detection algorithm by mean shift (MS) Introducing segmentation to further evolve objects Separated by Help with depth information derived from stereo Fixed number of sliding window templates There is a vision that applies. It is also possible for example Supervised learning in problem implementation Use decision tree or SVM in detail Learning conducted in Malay Shahet.al [8]. Xinyi Zhouet.…”

Section: Letrature Reviewmentioning

confidence: 99%

Real Time Object Detection Using Deep Learning

Padigel¹

2022

IJRASET

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Visually impaired people have difficulty moving safely and independently, which interferes with normal indoor and outdoor work and social activities. Similarly, they have a hard time identifying the basics of the environment. This paper presents a model for detecting the brightness and key colors of real-time images using the RGB method with an external camera and identifying basic objects and face recognition from human datasets.[2]. Object detection is a department of pc imaginative and prescient that appears for times of lexical entities in photographs and videos. The gadget makes use of the ESP-32 Cam's digital digicam to continuously seize severa frames, which can be sooner or later converted to audio segments. In this project, we use the You Only Look Once V3 (YOLO v3)algorithm, which runs thru a version of a really complex Convolutional Neural Network structure with OpenCV. Then with the aid of using the usage of Google Text to Speech, we convert the photo to textual content and afterwards textual content - to - speech for the visually impaired individual. Thus, the Visually Impaired individual receives the place of the gadgets withinside the digital digicam's view through audio. Distance calculation is aided with the aid of using an ultrasonic sensor. By the usage of The amassed consequences show that the proposed prototype is a hit in presenting visually impaired customers with the cappotential to realise surprising settings the usage of a user-pleasant machine that integrates this unique item detection Model

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“…For this implementation, PiCameras were used to capture images and locations at regular intervals. Regarding the training method used, the choice of the supervised learning (55) approach is evident, and in the image classifier (built with a pretrained neural network (56) ) to obtain better results augmentation and dropout processes are used. Their implementation presented a good result, reaching results on the test data set accuracy of 87%.…”

Section: Physical Security Systems Based On Iotmentioning

confidence: 99%

Revisión del uso de tecnologías y dispositivos IoT en los sistemas de seguridad física

Gómez

Echeverry

Sánchez

2021

inycomp

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En la actualidad ha habido un incremento en la inseguridad y robos a viviendas, edificios y diversos lugares sociales, lo que ha llevado a la búsqueda e implementación de mecanismos que permitan proteger a las personas y a estos sitios. De esta manera, las casas con Sistemas de Seguridad Física (SSF) son menos afectados por estos hechos, y gracias al avance tecnológico y la aparición del Internet de las Cosas (IoT) dichos sistemas han mejorado bastante, llegando a suplir varias necesidades con funcionalidades novedosas, que son posibles por los nuevos dispositivos y tecnologías. En este sentido, es deseable resolver el cuestionamiento sobre ¿cuáles son los dispositivos y tecnologías del IoT más comúnmente utilizados en diferentes diseños de SSF? Así pues, el propósito de este artículo es presentar los resultados de la revisión literaria hecha, en la que se buscó identificar los elementos comunes utilizados en los SSF modernos basados en IoT, destacando las ventajas, desventajas y limitaciones de los sistemas implementados.

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Object detection using deep neural networks

Cited by 27 publications

References 2 publications

Flexible neural network for fast and accurate road scene perception

Flexible neural network for fast and accurate road scene perception

Real Time Object Detection Using Deep Learning

Revisión del uso de tecnologías y dispositivos IoT en los sistemas de seguridad física

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