특징 융합과 공간 강조를 적용한 딥러닝 기반의 개선된 YOLOv4S
Modified YOLOv4S based on Deep learning with Feature Fusion and Spatial Attention
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
본 논문은 특징 융합과 공간 강조를 적용하여 작고 페색된 객체 검출을 위한 개선된 YOLOv4S를 제안하였다. 기존 YOLOv4S은 경량 네트워크로 깊은 네트워크 대비 특징 추출 능력 부족하다. 제안하는 방법은 먼저 feature fusion으로 서로 다른 크기의 특징맵을 결합하여 의미론적 정보 및 저수준 정보를 개선하였다. 또한, dilated convolution으로 수용 영역을 확장하여 작고 폐색된 객체에 대한 검출 정확도를 향상시켰다. 두 번째로 spatial attention으로 기존 공간 정보 개선하여 객체간 구분되어 폐색된 객체의 검출 정확도를 향상시켰다. 제안하는 방법의 정량적 평가를 위해 PASCAL VOC 및 COCO 데이터세트를 사용하였다. 실험을 통해 제안하는 방법은 기존 YOLOv4S 대비 PASCAL VOC 데이터세트에서 mAP 2.7% 및 COCO 데이터세트에서 mAP 1.8% 향상되었다.
Abstract
In this paper proposed a feature fusion and spatial attention-based modified YOLOv4S for small and occluded detection. Conventional YOLOv4S is a lightweight network and lacks feature extraction capability compared to the method of the deep network. The proposed method first combines feature maps of different scales with feature fusion to enhance semantic and low-level information. In addition expanding the receptive field with dilated convolution, the detection accuracy for small and occluded objects was improved. Second by improving the conventional spatial information with spatial attention, the detection accuracy of objects classified and occluded between objects was improved. PASCAL VOC and COCO datasets were used for quantitative evaluation of the proposed method. The proposed method improved mAP by 2.7% in the PASCAL VOC dataset and 1.8% in the COCO dataset compared to the Conventional YOLOv4S.
참고문헌 (15)
- 신석용, "딥러닝 기반의 Semantic Segmentation을 위한 Residual U-Net에 관한 연구", 디지털융복합연구, 19(6), pp.251~257 (2021), 한국디지털정책학회[DOI]
- 박찬수, "효율적인 객체 검출을 위해 Attention Process를 적용한 경량화 모델에 대한 연구", 디지털융복합연구, 19(5), pp.307~313 (2021), 한국디지털정책학회[DOI]
- K. He, "Mask R-CNN", IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), pp.386~397 (2020)
- J. Qu, "Dilated convolution and feature fusion SSD network for small object detection in remote sensing images", IEEE Access, 8, pp.82832~82843 (2020)
- S. Shin, "Improved YOLOv3 with duplex FPN for object detection based on deep learning", The International Journal of Electrical Engineering & Education (2021)
- M. Tan, "EfficientDet : Scalable and Efficient Object Detection", 2020IEEE, 2020, 10778~10787
- C. Y. Wang, "Scaled-YOLOv4: Scaling Cross Stage Partial Network", Proceedings of the IEEE, 2020, 13029~13038
- A. Bochkovskiy, "YOLOv4 : Optimal Speed and Accuracy of Object Detection", arXiv preprint arXiv:2004.10934., ~ (2020)
- S. Liu, "Path Aggregation Network for Instance Segmentation", 2018 IEEE, 2018, 8759~8768
- Woo, S., "CBAM: Convolutional block attention module", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11211, pp.3~19 (2018)
- J. Redmon, "YOLOv3 : An Incremental Improvement" (2018), ArXiv
- C. Wang, "CSPNet: A New Backbone that can Enhance Learning Capability of CNN", Proceedings of the IEEE, 2019, 390~391
- Z. Zheng, "Distance-IoU Loss : Faster and Better Learning for Bounding Box Regression", Proceedings of the AAAI Conference on Artificial Intelligence, 34(7), pp.12993~13000 (2020)
- M. Everingham, "The Pascal Visual Object Classes(VOC)Challenge", International Journal of Computer Vision, 88(2), pp.303~338 (2010)
- T. Y. Lin, "Microsoft COCO: Common Objects in Context", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8693, pp.740~755 (2014)