웨이블릿 변환을 활용한 효율적인 의미론적 분할 기술
Efficient Semantic Segmentation Using Wavelet-transform
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초록
의미론적 영상 분할 기술은 오브젝트 검출과 더불어 자율주행 차량의 주변 환경 인식에 많이 사용되고 있다. 제한된 장비와 자원을 사용하는 자율주행 특성상 가볍고 빠른 네트워크가선호되는데, 본 논문에서는 웨이블릿 변환을 활용하여 효율적인 의미론적 영상 분할을 하는방법을 제안한다. 먼저 웨이블릿 변환을 사용하여 영상 데이터를 고주파, 저주파 성분으로 나누어 주고, 각각의 성분에 대하여 서로 다른 특징지도 추출을 하여 서로 다른 정보를 적합하게합쳤다. 자율주행에 적합한 가벼운 네트워크를 베이스라인으로 Cityscapes 데이터 세트에 제안된 방식을 적용했을 때, 0.2%의 파라미터 증가를 통해 2.2% 성능향상을 달성했다. 이 같은 알고리즘을 활용하여 더욱더 안정적이고 정확한 주변 환경 인식에 적용되길 기대한다.
Abstract
Semantic segmentation and object detection are widely used to perceive surrounding environment during autonomous driving. Owing to the nature of autonomous driving, which operates with limited resources and equipment, lightweight and fast networks are preferred. In this paper, we propose an efficient semantic segmentation algorithm using a wavelet transform. First, we apply the wavelet transform to separate high-frequency and low-frequency components from an input image. For each component, different feature maps are extracted, and the distinct information appropriately merged. When the proposed method was applied to the Cityscapes dataset using a lightweight network suitable for autonomous driving, a 2.2% performance improvement was achieved from a 0.2% parameter increase. We expect this algorithm can be applied to achieve more stable and accurate perceptions of the surrounding environment.
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