%A Yunyang YAN,Chenxi DU,Yian LIU,Shangbing GAO %T Fire detection based on lightweight convolutional neural network %0 Journal Article %D 2020 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2019.424 %P 100-107 %V 50 %N 2 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1915.shtml} %8 2020-04-20 %X

A novel lightweight flame detection method was proposed based on MobileNet. The video flame detection rate was promoted by the feature receptive field of DCB(dilated convolution block)module expand based on depthwise separable convolution and dilated convolution to strengthen the feature semantic information. The SSD(single shot multibox detector) detection framework was also optimized. The lightweight detection model DMSSD(Dilated MobileNet-SSD) was provided. Experiments showed that the mean average precision was increased by 1.7% and 3.8% respectively on the PASCAL VOC dataset and the VisiFire dataset of Bilkent University. Furthermore, the detection speed was up to 80 frames per second. The robustness and real-time performance of DMSSD were strong.