Abstract
Recently, Convolutional Neural Networks (CNNs) have been used for object/target (e.g., face, person, car, pedestrian, road sign) detection with great results. However, using such CNN models on embedded processors for real-time processing is prohibited by HW constraints. In that sense, various architectures and settings will be examined in order to facilitate and accelerate the use of embedded CNN-based object detectors with limited computational capabilities. The following target detection topics will be presented: Object detection as search and classification task. Detection as classification and regression task. Modern architectures for target detection (e.g., RCNN, Faster-RCNN, YOLO v4, SSD, RetinaNet, RBFNet, CornerNet, CenterNet, DETR), Lightweight architectures, Data augmentation and Deployment are presented in detail. Evaluation and benchmarking measures are detailed.
Boat detection.
Object detection performance.
Deep-Object-Detection-v3.8-SummaryUnderstanding Questionnaire
https://docs.google.com/forms/deep-object-detection