Indoor Smoking Behavior Detection Based on YOLOv3-tiny

Zhao, Rentao; Wang, Mengyi; Zilong, Zhai; Li, Ping; Zeng, Qingyu

doi:10.1109/cac48633.2019.8996951

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…Few researchers have incorporated computer vision to solve this problem. To solve the problem of traditional supervision and the low precision of smoke alarms in an indoor environment, Rentao et al [41] presented a deep learning-based solution based on YOLOv3-tiny, named Improved YOLOv3-tiny. Their proposed method inputs the images from their own created image dataset in an indoor environment.…”

Section: Literature Reviewmentioning

confidence: 99%

CNN-Based Smoker Classification and Detection in Smart City Application

Khan

Hassan

et al. 2022

Sensors

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To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time.

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Section: Literature Reviewmentioning

confidence: 99%

CNN-Based Smoker Classification and Detection in Smart City Application

Khan

Hassan

et al. 2022

Sensors

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“…YOLOv3 merupakan model deteksi objek yang diusulkan oleh Redmon J dengan mengimplementasikan deep learning dan membangun hubungan langsung antara input gambar asli dengan output, serta memiliki kecepatan dan presisi yang tinggi. Algorima ini menggunakan Darknet-53 sebagai backbone untuk mengekstrasi fitur (Rentao, et al, 2019). Arsitektur YOLOv3 menggunakan convolutional layer 1x1 dan convolutional layer 3x3 untuk mengekstraksi fitur (ADARSH, et al, 2020).…”

Section: Yolov3unclassified

“…Sehingga kecepatan meningkat dari versi YOLO sebelumnya, tetapi akurasi deteksi semakin berkurang (ADARSH, et al, 2020). Backbone jaringan YOLOv3-Tiny yaitu terdiri dari convolutional layer dan pooling layer (Rentao, et al, 2019). Metode ini dapat meningkatkan informasi fitur objek yang terdapat pada gambar (Yang, et al, 2019).…”

Section: Yolov3-tinyunclassified

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Implementasi Algoritma You Only Look Once (YOLO) untuk Deteksi Korban Bencana Alam

Sarosa

Muna

2021

JTIIK

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Bencana alam merupakan suatu peristiwa yang dapat menyebabkan kerusakan dan menciptakan kekacuan. Bangunan yang runtuh dapat menyebabkan cidera dan kematian pada korban. Lokasi dan waktu kejadian bencana alam yang tidak dapat diprediksi oleh manusia berpotensi memakan korban yang tidak sedikit. Oleh karena itu, untuk mengurangi korban yang banyak, setelah kejadian bencana alam, pertama yang harus dilakukan yaitu menemukan dan menyelamatkan korban yang terjebak. Penanganan evakuasi yang cepat harus dilakukan tim SAR untuk membantu korban. Namun pada kenyataannya, tim SAR mengalami kendala selama proses evakuasi korban. Mulai dari sulitnya medan yang dijangkau hingga terbatasnya peralatan yang dibutuhkan. Pada penelitian ini sistem diimplementasikan untuk deteksi korban bencana alam yang bertujuan untuk membantu mengembangkan peralatan tim SAR untuk menemukan korban bencana alam yang berbasis pengolahan citra. Algoritma yang digunakan untuk mendeteksi ada atau tidaknya korban pada gambar adalah You Only Look Once (YOLO). Terdapat dua macam algoritma YOLO yang diimplementasikan pada sistem yaitu YOLOv3 dan YOLOv3 Tiny. Dari hasil pengujian yang telah dilakukan didapatkan F1 Score mencapai 95.3% saat menggunakan YOLOv3 dengan menggunakan 100 data latih dan 100 data uji. Abstract Natural disasters are events that can cause damage and create havoc. Buildings that collapse and can cause injury and death to victims. Humans can not predict the location and timing of natural disasters. After the natural disaster, the first thing to do is find and save trapped victims. The handling of rapid evacuation must be done by the SAR team to help victims to reduce the amount of loss due to natural disasters. But in reality, the process of evacuating victims of natural disasters is still a lot of obstacles experienced by the SAR team. It was starting from the difficulty of the terrain that is reached to the limited equipment needed. In this study, a natural disaster victim detection system was designed using image processing that aims to help find victims in difficult or vulnerable locations when directly reached by humans. In this study, a detection system for victims of natural disasters was implemented which aims to help develop equipment for the SAR team to find victims of natural disasters based on image processing. The algorithm used is You Only Look Once (YOLO). In this study, two types of YOLO algorithms were compared, namely YOLOv3 and YOLOv3 Tiny. From the test results that have been obtained, the F1 Score reaches 95.3% when using YOLOv3 with 100 training data and 100 test data.

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“…Weibin Cai et alused mosaic data augmentation to avoid model overfitting which is caused by the simple background in the public datasets and the YOLO-SMOKE model outperforms the original model by 4.91% mAP [6]. Rentao Zhao et al proposed an improved algorithm based on YOLOv3-tiny deep learning network for indoor smoking behavior detection, which can effectively meet the practical application requirements and provide a new way for assisting indoor supervision [7].…”

Section: Introductionmentioning

confidence: 99%

A smoking behavior detection method based on the YOLOv5 network

Jiang

Liu

et al. 2022

J. Phys.: Conf. Ser.

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Smoking in public places not only brings about some safety hazards, but also does harm to people’s lives, property and living environment. A smoking behavior detection model based on deep learning is trained for the concern of environment and safety. First, a vertical rotation data enhancement method is adopted in the preprocessing stage to extend the dataset and increase the objects of detection. Then, the channel attention module is introduced in backbone network to calibrate the feature response. Finally, added a small target detection layer to the YOLOv5 algorithm. This paper analyzes the network structure of the YOLOv5s, and the model is trained and tested by utilizing the YOLOv5s network. Experimental results show that the mAP value of the algorithm is improved by 5.3% over the original algorithm.

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Indoor Smoking Behavior Detection Based on YOLOv3-tiny

Cited by 17 publications

References 10 publications

CNN-Based Smoker Classification and Detection in Smart City Application

CNN-Based Smoker Classification and Detection in Smart City Application

Implementasi Algoritma You Only Look Once (YOLO) untuk Deteksi Korban Bencana Alam

A smoking behavior detection method based on the YOLOv5 network

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