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Flame Image Recognition Based on Lightweight Convolutional Neural Network

Aiming at the imbalance between judgment accuracy and real-time performance of existing flame recognition models on devices with limited memory and computing capacity, a flame recognition model based on lightweight convolutional neural network YOLOV2 was designed. By using MobileNetV2 as the backbone feature extraction network of the target detection network YOLOV2, while modifying specific parameters, the amount of network parameters is greatly reduced. Experiments show that the accuracy of the detection model reaches 91.6%, the mAP index is 94.32%, the prediction time is 45 frames per second, and the model size is only 13MB, which effectively balances the accuracy and real-time performance of flame detection.