Fire Fighter Robot with Deep Learning and Machine Vision

Dhiman, Amit; Shah, Neel; Adhikari, Pranali; Kumbhar, Sayli; Dhanjal, Inderjit Singh; Mehendale, Ninad

doi:10.2139/ssrn.3633609

Cited by 5 publications

(2 citation statements)

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“…The authors [13] developed a vision-based algorithm for on-site detection of fire and a mechanism for an autonomous response towards the point of detection. An intelligent vision-based robot that can detect and classify fire using a convolutional neural network technique implemented on a raspberry pi was presented in Dhiman et al [14]. The obstacle avoidance was designed with an ultrasonic sensor, while remote communication was handled by a GSM module [15].…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Design of a Vision-Based Fire Fighting Robot for Smart City Application

Ejidokun¹,

Omitola²,

Fiyinfoluwa³

et al. 2022

JESA

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In the world today, fire incidence has been a frequent occurrence, this has caused the loss of many lives. Also, valuable properties, public utilities, and facilities have been destroyed. The study conceptualized a proposed design of an intelligent vision based robot for curbing the menace caused by fire outbreaks. The proposed design entails consistent remote interaction between a robot and sensor nodes. The robot node denotes the firefighting robot situated in the fire station. In its idle state, it operates in a passive node, by listening to the incoming beacons from the sensor nodes. The sensor nodes installed on designated sites consists of flame, wireless sensors with a mini-controller. Whenever the robot receives a distress alert from any of the sensor nodes, it automatically switches to an active mode and simultaneously navigates to the location in distress. The activity of the firefighting robot can be remotely monitored and controlled in real-time by a human operator via androidbased application. However, modifications have been proposed, based on the identified flaws of existing systems. Successful implementation of this design will provide a reliable and efficient means of monitoring multiple sites in real-time and also ensure environmental safety.

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Section: Introductionmentioning

confidence: 99%

A Conceptual Design of a Vision-Based Fire Fighting Robot for Smart City Application

Ejidokun¹,

Omitola²,

Fiyinfoluwa³

et al. 2022

JESA

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“…The firefighting robot based on deep learning for fire detection developed in the paper [17]. The system combined Alexnet model and Imagenet for fire detection after training and testing the model.…”

Section: Introductionmentioning

confidence: 99%

Developing a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network

Saif

Muneer

Abdulrahman³

et al. 2022

TAPR

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The object of research is a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network. Within the recent few decades, fires are considered as one of the most serious disaster that occurs in many places around the world. The severity of fire incidents causes damages to buildings, infrastructures and properties. Resulting losses of human’s life and costs them a lot of losses. Thus, fire poses a great threat to us significantly; it is extremely dangerous for fire fighters. Fires can be resulted by materials such as rubber and chemical products. Other sources of fire are the short circuits on electrical devices and faults in power circuits. Additionally, overheating and overloading problems can be the cause of fire incidents. All these reasons lead to bad consequences when there is no immediate response to such problems. The advent of computer vision technology has played such a significant role for human life. Artificial intelligence field has improved the efficiency and behaviors of robotics beyond expectations. The interference of artificial intelligence made robotics act intelligently. For this reason, in this paper we presented a mobile robot based on deep learning to detect the fire source and determines its coordinate position then automatically moves toward the target and extinguish fire. Deep learning algorithms are the efficient ones for object detection applications. CNN model is one of the most common deep learning algorithms which have been used in the study for the fire detection. Due to the insufficient amount of datasets and large efforts required to build model from scratch. MobileNet V2 is one of the CNN models that support transfer learning technique. After training the model and testing it on 20 % of the used datasets the classification accuracy achieved up to 98.01 %. The motion repeatability of the robot has been implemented and tested resulting mean error 0.648 cm.

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A Deep Learning CNN Approach Regarding Drone Surveillance in Fire-Fighting Scenarios

Travediu,

Vladareanu,

Munteanu

et al. 2024

Signals and Communication Technology

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Fire Fighter Robot with Deep Learning and Machine Vision

Cited by 5 publications

References 0 publications

A Conceptual Design of a Vision-Based Fire Fighting Robot for Smart City Application

A Conceptual Design of a Vision-Based Fire Fighting Robot for Smart City Application

Developing a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network

A Deep Learning CNN Approach Regarding Drone Surveillance in Fire-Fighting Scenarios

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