Real-Time Identification of Artifacts: Synthetic Data for AI Model

The collections represent the constitutive element and the raison d'être of each museum. Their management , care and dissemination are therefore a task of primary importance for every museum. Applying new Artificial Intelligence technologies in this area could lead to new initiatives. However, the development of certain tools requires structured and labeled datasets for the training phases which are not always easily available. The proposed contribution is within the domain of the construction of specific datasets with low budget tools and explores the results of a first step in this direction by testing algorithms for the recognition and labeling of heritage objects. The developed workflow is part of a first prototype that could be used both in heritage dissemination or gamification applications, and for use in heritage research tools

Occluded object detection using deep learning by image synthesis with hands

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 이혁재.최근 딥 러닝을 이용한 물체 인식을 통한 다양한 응용 시스템 개발이 활발하다. 본 연구에서는 스마트 냉장고 재고 관리를 목적으로 하여, 냉장고 내외부로 이동하는 손 안의 상품 인식률을 증가시키기 위한 효율적인 학습데이터 생성 방법을 제안한다. 냉장고 내부의 고정된 카메라 관점에서 손에 의해 옮겨지는 상품을 인식할 때, 몇 가지 어려움이 있다. 이동하는 위치에 따른 상품의 크기, 모양 등의 다양성이 생기며, 상품이 항상 손에 잡혀져 있기 때문에 가려짐(Occlusion)이 발생한다. 냉장고 내부와 외부 환경이 다르기 때문에 배경의 다양성 문제도 생긴다. 이러한 모든 경우를 고려하여 수작업으로 학습데이터를 생성하는 것은 너무 많은 시간과 노동력이 소모된다. 하지만, 배경이 제거된 손 이미지를 물체와 합성한 후, 다양한 배경을 입혀서 상품에 대한 라벨링 데이터를 자동으로 생성한다면, 많은 시간과 노동력 소모 없이도 위의 문제를 해결할 수 있다. 수작업으로 라벨링을 한 소량의 학습데이터의 경우, 52.03%의 mean Average Precision(mAP)를 보인다. 우리가 제안하는 방법을 사용하여 학습데이터를 생성한 후, 수작업으로 라벨링한 소량의 학습데이터와 합쳐서 학습한 결과, 수작업으로 라벨링하는 노동 없이도 87.29%의 mAP를 달성할 수 있었다. 또한, 손 이미지 없이 합성한 경우와 비교했을 때, 5.47%의 성능 향상을 보여주었으며, Z. Zhong et al이 제안한 random erasing 기법과 비교하여 7.21%의 성능 증가를 보여주었다[22].Recently, various application systems have been developed for object detection based on depp learning. For the purpose of inventory management of smart refrigerators, this paper proposes an efficient method for generating training datasets that improves detection rate of products held by the hand when being placed in or removed from a refrigerator. In this case, there are several issues for detecting a product. The size and appearance of the target product change as the position of the product. Occlusion problems occur since products are partially covered by the hand. The difference between the interior and exterior backgrounds of the refrigerator makes it difficult to recognize objects. Considering above problems, generating the datasets manually requires significant time and effort. To solve this problem, we create synthetic images and labels by merging images of objects, images of hands, and various backgrounds. The detector learned with a few manually labeled dataset gives 52.03% mAP. When adding the dataset of synthetic images with hands to a few manually labeled dataset, the detector obtains improved mAP of 87.29% without the labor of manual labeling. We achieved a higher performance by 5.47% compared to image synthesis without hands, and by 7.21% compared to Z. Zhong et als random erasing technique.제 1 장 서 론 1 1.1 연구의 배경 1 1.2 연구의 내용 3 1.3 논문 구성 3 제 2 장 관련 연구 5 2.1 딥 러닝 모델 관련 연구 5 2.2 이미지 합성 관련 연구(Image synthesis) 7 2.3 자동 라벨링 관련 연구 8 2.4 가려짐 관련 연구(Occlusion) 9 제 3 장 제안 방법 및 구현 12 3.1 문제점 정의 12 3.2 제안 방법 16 3.3 합성 과정(Synthesis process) 19 제 4 장 실 험 28 4.1 실험 환경 및 구성 28 4.2 실험 결과 34 제 5 장 결 론 42 참고문헌 44 Abstract 50Maste

Synthetic Examples Improve Generalization for Rare Classes

The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars. Few-shot learning is an open problem: current computer vision systems struggle to categorize objects they have seen only rarely during training, and collecting a sufficient number of training examples of rare events is often challenging and expensive, and sometimes outright impossible. We explore in depth an approach to this problem: complementing the few available training images with ad-hoc simulated data.Our testbed is animal species classification, which has a real-world long-tailed distribution. We present two natural world simulators, and analyze the effect of different axes of variation in simulation, such as pose, lighting, model, and simulation method, and we prescribe best practices for efficiently incorporating simulated data for real-world performance gain. Our experiments reveal that synthetic data can considerably reduce error rates for classes that are rare, that as the amount of simulated data is increased, accuracy on the target class improves, and that high variation of simulated data provides maximum performance gain

Automated production of synthetic point clouds of truss bridges for semantic and instance segmentation using deep learning models

The cost of obtaining large volumes of bridge data with technologies like laser scanners hinders the training of deep learning models. To address this, this paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The method generates point clouds by specifying the dimensions and components of the bridge, resulting in high variability in the generated dataset. A deep learning model is trained using the generated point clouds, which is an adapted version of JSNet. The accuracy of the results surpasses previous heuristic methods. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges. Furthermore, the success of the deep learning approach suggests its potential for semantic and instance segmentation of complex point clouds beyond truss bridges.Agencia Estatal de Investigación | Ref. PID2021-124236OB-C33Agencia Estatal de Investigación | Ref. RYC2021-033560-IUniversidade de Vigo/CISU

An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model

Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA