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 Summary