ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (16), p.7086
• Main Authors: Li, Bin, Liu, Peng
• Format: Article
• Language: English
• Subjects: Algorithms; anchor-free detection network; deformable convolution; smoke and fire detection; Telecommunication systems; Telematics; Unmanned aerial vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23167086

In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input images, which is composed of ResNet added to deformable convolution. The neck network, which is responsible for multi-scale detection, is composed of the feature pyramid network. The head network outputs results and adopts pseudo intersection over union combined with anchor-free network structure. The head network consists of two full convolutional subnetworks: the first is the classification sub-network, which outputs a classification confidence score, and the second is the regression sub-network, which predicts the parameters of bounding boxes. The deformable convolution (DCN) added to the backbone network enhances the shape feature extraction capability for fire and smoke, and the pseudo intersection over union (pseudo-IoU) added to the head network solves the label assignment problem that exists in anchor-free object detection networks. The proposed ADFireNet is evaluated using the fire smoke dataset. The experimental results show that ADFireNet has higher accuracy and faster detection speeds compared with other methods. Ablation studies have demonstrated the effectiveness of DCN and pseudo IoU.