FlameNet: A Real-Time, Lightweight Fire & Smoke Detection Solution for Internet of Vehicles

The growth of the Internet of Vehicles (IoV) has introduced new challenges and opportunities. Among the most crucial considerations is ensuring the safety of passengers and the environment. Connected vehicles offer numerous benefits, but they are also at risk of fire incidents caused by various factors such as electrical failures, fuel leaks, and collisions. These events can result in devastating outcomes, including property loss, injury, and even loss of life. The conventional fire detection systems employed in vehicles are large, expensive, and consume a considerable amount of power, making them incompatible with the resource-limited environment of the IoV. The present work overcomes these limitations with the introduction of FlameNet, a custom-designed neural network for fire detection. FlameNet not only outperforms existing solutions but also boasts a lightweight design, which contributes to its high computational efficiency, allowing it to run smoothly on low-cost embedded devices with a frame rate of 28 frames per second. Accuracy, recall, precision, and F-measure were used to assess the model’s efficiency on both industry-standard fire datasets and a custom-built test set. The results showed that FlameNet performed well on both datasets, with its performance being better on the standard fire test dataset due to its limited image diversity. The performance of the model is encouraging, and the IoT functionality allows immediate visual feedback and a fire alarm in the event of an emergency.