Auswahl der wissenschaftlichen Literatur zum Thema „TensorFlow Object Detection API 2“

Recent developments in the field of object detection have highlighted a significant variation in quality between visual datasets. As a result, there is a need for a standardized approach of validating visual dataset features and their performance contribution. With a focus on vehicle detection, this thesis aims to develop an evaluation method utilized for comparing visual datasets. This method was utilized to determine the dataset that contributed to the detection model with the greatest ability to detect vehicles. The visual datasets compared in this research were BDD100K, KITTI and Udacity, each one being trained on individual models. Applying the developed evaluation method, a strong indication of BDD100K's performance superiority was determined. Further analysis and feature extraction of dataset size, label distribution and average labels per image was conducted. In addition, real-world experimental conduction was performed in order to validate the developed evaluation method. It could be determined that all features and experimental results pointed to BDD100K's superiority over the other datasets, validating the developed evaluation method. Furthermore, the TensorFlow Object Detection API's ability to improve performance gain from a visual dataset was studied. Through the use of augmentations, it was concluded that the TensorFlow Object Detection API serves as a great tool to increase performance gain for visual datasets.<br>Inom fältet av objektdetektering har ny utveckling demonstrerat stor kvalitetsvariation mellan visuella dataset. Till följd av detta finns det ett behov av standardiserade valideringsmetoder för att jämföra visuella dataset och deras prestationsförmåga. Detta examensarbete har, med ett fokus på fordonsigenkänning, som syfte att utveckla en pålitlig valideringsmetod som kan användas för att jämföra visuella dataset. Denna valideringsmetod användes därefter för att fastställa det dataset som bidrog till systemet med bäst förmåga att detektera fordon. De dataset som användes i denna studien var BDD100K, KITTI och Udacity, som tränades på individuella igenkänningsmodeller. Genom att applicera denna valideringsmetod, fastställdes det att BDD100K var det dataset som bidrog till systemet med bäst presterande igenkänningsförmåga. En analys av dataset storlek, etikettdistribution och genomsnittliga antalet etiketter per bild var även genomförd. Tillsammans med ett experiment som genomfördes för att testa modellerna i verkliga sammanhang, kunde det avgöras att valideringsmetoden stämde överens med de fastställda resultaten. Slutligen studerades TensorFlow Object Detection APIs förmåga att förbättra prestandan som erhålls av ett visuellt dataset. Genom användning av ett modifierat dataset, kunde det fastställas att TensorFlow Object Detection API är ett lämpligt modifieringsverktyg som kan användas för att öka prestandan av ett visuellt dataset.

Tato diplomová práce se zabývá detekcí prvků uživatelského rozhraní na obrázku displejetiskárny za použití konvolučních neuronových sítí. V teoretické části je provedena rešeršesoučasně používaných architektur pro detekci objektů. V praktické čísti je probrána tvorbagalerie, učení a vyhodnocování vybraných modelů za použití Tensorflow ObjectDetectionAPI. Závěr práce pojednává o vhodnosti vycvičených modelů pro zadaný úkol.

Object Detection is widely used in many applications such as face detection, detecting vehicles and pedestrians on streets, and autonomous vehicles. Object detection not only includes recognizing and classifying objects in an image, but also localizes those objects and draws bounding boxes around them. Therefore, most of the successful object detection networks make use of neural network based image classifiers in conjunction with object detection techniques. Tensorflow Object Detection API, an open source framework based on Google's TensorFlow, allows us to create, train and deploy object detection models. This thesis mainly focuses on detecting objects kept in a refrigerator. To facilitate the object detection in a refrigerator, we have used Tensorflow Object Detection API to train and evaluate models such as SSD-MobileNet-v2, Faster R-CNN-ResNet-101, and R-FCN-ResNet-101. The models are tested as a) a pre-trained model and b) a fine-tuned model devised by fine-tuning the existing models with a training dataset for eight food classes extracted from the ImageNet database. The models are evaluated on a test dataset for the same eight classes derived from the ImageNet database to infer which works best for our application. The results suggest that the performance of Faster R-CNN is the best on the test food dataset with a mAP score of 81.74%, followed by R-FCN with a mAP of 80.33% and SSD with a mAP of 76.39%. However, the time taken by SSD for detection is considerably less than the other two models which makes it a viable option for our objective. The results provide substantial evidence that the SSD model is the most suitable model for deploying object detection on mobile devices with an accuracy of 76.39%. Our methodology and results could potentially help other researchers to design a custom object detector and further enhance the precision for their datasets.<br>Graduate