• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.461-463
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• 2023

본 연구는 실시간 MOT(Multiple-Object-Tracking)의 성능을 향상시키기 위해 다양한 기법을 적용한 MOT 모델을 설계한다. 연구에서 사용하는 Backbone 모델은 TBD(Tracking-by-Detection) 기반의 Tracking 모델을 사용한다. Heatmap Detection을 통해 객체를 검출하고 Transformer 기반의 Feature를 연결하여 Tracking 한다. 제안하는 방법은 Anchor 기반의 Detection의 장시간 문제와 추적 객체 정보 전달손실을 감소하여 실시간 객체 추적에 도움이 될 것으로 사료된다.

• 자동차안전학회지
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• 제16권1호
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• pp.29-34
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• 2024

This paper presents a multi-label lane detection method for autonomous vehicles based on deep learning. The proposed algorithm can detect two types of lanes: center lane and normal lane. The algorithm uses a convolution neural network with an encoder-decoder architecture to extract features from input images and produce a multi-label heatmap for predicting lane's label. This architecture has the potential to detect more diverse types of lanes in that it can add the number of labels by extending the heatmap's dimension. The proposed algorithm was tested on an OpenLane dataset and achieved 85 Frames Per Second (FPS) in end to-end inference time. The results demonstrate the usability and computational efficiency of the proposed algorithm for the lane detection in autonomous vehicles.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1020-1025
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• 2021

Facial alignment is very important task for human life. And facial landmark detection is one of the instrumental methods in face alignment. We introduce the stacked hourglass networks with transposed convolutional layers for facial landmark detection. our method substitutes nearest neighbor upsampling for transposed convolutional layer. Our method returns better accuracy in facial landmark detection compared to stacked hourglass networks with nearest neighbor upsampling.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.541-543
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• 2022

In recent years, retinal disorders have become a serious health concern. Retinal disorders develop slowly and without obvious signs. To avoid vision deterioration, early detection and treatment are critical. Optical coherence tomography (OCT) is a non-invasive and non-contact medical imaging technique used to acquire informative and high-resolution image of retinal area and underlying layers. Disease signs are difficult to detect because OCT images have many areas which are not related to any disease. In this paper, we present a deep learning-based method to perform multi-label classification on a long-tailed OCT dataset. Our method first extracts the region of interest and then performs the classification task. We achieve 98% accuracy, 92% sensitivity, and 99% specificity on our private OCT dataset. Using the heatmap generated from trained convolutional neural network, our method is more robust and explainable than previous approaches because it focuses on areas that contain disease signs.