초기 화재 탐지에서 소형 객체 검출 향상을 위한 이미지 업스케일링 결합 다단계 탐지 기법
A Multi-Stage Detection with Image Upscaling for Enhancing Small Object Detection in Early Fire Detection
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초록
Early-stage fires appear small in captured images, making accurate detection of small objects crucial for rapid response. Although convolution, pooling operations in convolutional neural networks enhance high-level target recognition, they can also lead to the loss of fine-grained details, thereby degrading the representation and detection performance of small objects. In this paper, we propose a multi-stage detection framework “U-FRCNN (Upscaled-Faster R-CNN)” that combines Faster R-CNN with image upscaling to enhance low-confidence score regions. Initially, objects are detected with Faster R-CNN; then, regions with low detection scores are upscaled and re-evaluated using the same model. Experiments on a dataset from Roboflow and manually labeled images show that our approach improves small object mAP from 0.0947 to 0.1510 (a 59.4% increase) and overall mAP from 0.2183 to 0.2483 (a 13.7% increase), while large object detection remains robust. These results demonstrate the potential of our method for effective early fire detection.
Abstract
Early-stage fires appear small in captured images, making accurate detection of small objects crucial for rapid response. Although convolution, pooling operations in convolutional neural networks enhance high-level target recognition, they can also lead to the loss of fine-grained details, thereby degrading the representation and detection performance of small objects. In this paper, we propose a multi-stage detection framework “U-FRCNN (Upscaled-Faster R-CNN)” that combines Faster R-CNN with image upscaling to enhance low-confidence score regions. Initially, objects are detected with Faster R-CNN; then, regions with low detection scores are upscaled and re-evaluated using the same model. Experiments on a dataset from Roboflow and manually labeled images show that our approach improves small object mAP from 0.0947 to 0.1510 (a 59.4% increase) and overall mAP from 0.2183 to 0.2483 (a 13.7% increase), while large object detection remains robust. These results demonstrate the potential of our method for effective early fire detection.