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Detection transformer (DETR) combines convolutional neural network (CNN) with transformer, providing a more advanced idea. In this paper, an object detection model based on DETR is proposed for fire and smoke detection. Compared with other methods that based on deep learning, the proposed one simplifies the pipeline of detection and builds an end-to-end detector. At the same time, the original DETR model usually requires long training time and large amount of computation, resulting in relatively poor performance in detection speed and accuracy, and it shows to be not friendly for small or early fire detection. Therefore, when designing the proposed network model, a normalization-based attention module is added in the feature extraction stage to highlight the effective features, being beneficial to the process of encoding. A multiscale deformable attention is also used in the encoder–decoder structure, accelerating therefore, the process of convergence during the training of the model, which includes the enhancement of the detection of small objects. Also, considering the cost of computation, the number of layers in the encoder–decoder structure is redefined to reduce the complexity of the model, and that also reduces the requirements of the application equipment. Detailed experiments are conducted on three self-built datasets and two public video sets. The results show that, the proposed method has an excellent performance on all of the datasets considered here. [ABSTRACT FROM AUTHOR]

Having a tiny scale and few identifiable features, small objects are particularly difficult to detect, especially when the image resolution is not high. Further, large-scale variation across object instances places a burden on small object detection. In view of the insufficient detection ability of YOLOF (You Only Look One-level Feature) detector for small objects at low resolution, we propose an effective method, named YOLOFs, to boost small object detection precision in the case of object instances scale variation. The proposed three key modules (feature fusion module, visual perception module and feature encoding module) expand the receptive field of the network and greatly improve small object detection precision. Extensive experiments on the COCO, VOC, and Fire datasets prove the effectiveness of our method while keeping the detector simple and accurate. Without bells and whistles, our method outperforms YOLOF 5.1 AP (average precision), 6.2 AP, and 4.9 AP on small object detection at 704 × 704 , 608 × 608, and 512 × 512 resolutions, respectively, and it also achieves better performance on the VOC and Fire datasets. Our code will be made publicly available. [ABSTRACT FROM AUTHOR]