다중 스케일 특징맵 지식 증류를 통한 3D 객체 검출
3D Object Detection via Multi-Scale Feature Knowledge Distillation
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초록
본 연구에서는 모델의 경량화를 위해 교사 모델의 출력 특징맵에서 3D 객체의 정보를 추출해학생 모델의 다중 스케일 특징맵(Multi-scale feature map)에 맞게 증류하는 3D 객체 검출용 다중스케일 특징 지식 증류 기법인 M3KD (Multi-Scale Feature Knowledge Distillation for 3D Object Detection)를 제안한다. M3KD는 지식 증류 수행 시 학생 모델과 교사 모델의 다중 스케일 특징맵들 간 L2 손실(loss)을 사용해 특징맵 값의 차이를 줄이게 함으로써 학생 모델이 교사 모델의 백본을 모방하게 하여 학생 모델의 전체적인 정확도를 향상시키고, 기존의 이미지 분류 태스크(Task)에서 사용하는 클래스 로짓(Logits) 지식 증류를 적용해 교사 모델의 클래스 분류 로짓을 모방함으로써 학생 모델의 검출 정확도를 향상시킨다. 본 연구가 제안한 M3KD의 효과를 증명하기위해 KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) 데이터 셋에서 실험을 진행하였으며, 이때 학습한 학생 모델이 교사 모델 대비 30%의 추론 속도 향상을 달성하였다. 또한, 정확도에서 기존의 학생 모델과 비교시 모든 클래스 및 모든 난이도에서 평균적으로1.08%의 3D mAP (Mean Average Precision) 향상이 있음을 확인하였다. 또한 최신 지식 증류 기법인 PKD, SemCKD에 제안하는 기법을 추가로 적용하였을 시 기존 대비 0.42%, 0.52% 높은 정확도(3D mAP)를 나타내 성능 향상을 달성하였다.
Abstract
In this paper, we propose Multi-Scale Feature Knowledge Distillation for 3D Object Detection (M3KD), which extracting knowledge from the teacher model, and transfer to the student model consider with multi-scale feature map. To achieve this, we minimize L2 loss between feature maps at each pyramid level of the student model with the correspond teacher model so student model can mimic the teacher model backbone information which improves the overall accuracy of the student model. We apply the class logits knowledge distillation used in the image classification task, by allowing student model mimic the classification logits of the teacher model, to guide the student model to improve the detection accuracy. In KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset, our M3KD (Multi-Scale Feature Knowledge Distillation for 3D Object Detection) student model achieves 30% inference speed improvement compared to the teacher model. Additionally, our method achieved an average improvement of 1.08% in 3D mean Average Precision (mAP) across all classes and difficulty levels compared to the baseline student model. Furthermore, when integrated with the latest knowledge distillation methods such as PKD and SemCKD, our approach achieved an additional 0.42% and 0.52% improvement in 3D mAP, respectively, further enhancing performance.
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