객체 탐지 성능 향상을 위한 채널별 전처리 기법에 관한 연구
Research on Channel-Wise Preprocessing for Enhanced Infrared Object Detection
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초록
본 논문에서는 단일 채널(Channel)로 구성된 적외선(Infrared, IR) 영상이 RGB 기반 탐지 모델에 직접 적용하기 어렵다는 한계를 해결하고자 하였다. 기존에는 단일 채널을 단순히 3채널로 복제하는방식이 주로 사용되었으나, 이 방법은 정보 중복으로 인해 탐지 성능이 제한될 가능성이 있었다. 본연구는 이러한 한계를 극복하기 위해 적외선 영상의 단일 채널을 3채널로 복제한 뒤, 각 채널에CLAHE(Contrast Limited Adaptive Histogram Equalization), 라플라스 필터(Laplacian Filter), Top-hat 변환의 전처리 기법을 적용하여 탐지 성능을 높이는 방법을 제안한다. 본 연구에서는 RT-DETRv2 탐지 모델과 Anti-UAV300 데이터 세트(Dataset)를 사용해 10프레임(Frame) 간격으로 샘플링(Sampling) 한 적외선 영상을 실험에 활용하였다. 이를 통해 각 전처리 기법의 효과를 평가하고 최적 구성을 도출한 결과, mAP(mean Average Precision) 성능이 기존 방식 대비 2.2% 향상되었다. 이는 단순 복제 방식보다 성능이 개선됨을 확인한 것으로, 본 연구는 적외선 영상 기반 객체 탐지 성능 개선을 위한새로운 접근법을 제시하며, 향후 적외선 카메라가 사용되는 재난 상황, 야간 감시 정찰, 자율주행 차량의 저조도 환경에서의 객체 검출 분야에서 기여할 가능성이 클 것으로 기대된다.
Abstract
In this paper, we address the limitation of single-channel infrared (IR) images, which are difficult to directly apply to RGB-based detection models. Previously, a single channel was often replicated into three channels; however, this approach may limit detection performance due to information redundancy. To overcome this limitation, we propose a method that replicates the single-channel IR image into three channels, with each channel processed using different preprocessing techniques, such as CLAHE (Contrast Limited Adaptive Histogram Equalization), Laplacian Filter, and Top-hat transform, to improve detection performance. In this study, we utilized the RT-DETRv2 detection model and the Anti-UAV300 dataset, using IR images sampled at 10-frame intervals for our experiments. By evaluating the effects of each preprocessing technique and deriving the optimal configuration, our method achieved a 2.2% improvement in mean Average Precision (mAP) over conventional methods. This confirms that our method enhances performance over simple replication, presenting a novel approach to improving object detection performance in IR imaging, with promising applications across various fields, particularly in disaster situations where infrared cameras are utilized, as well as in nighttime surveillance and reconnaissance.
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