Sage Elliott, AI Engineer at Union.ai, presents the “Object Detection Models: Balancing Speed, Accuracy and Efficiency,” tutorial at the May 2025 Embedded Vision Summit.
Deep learning has transformed many aspects of computer vision, including object detection, enabling accurate and efficient identification of objects in images and videos. However, choosing the right deep neural network-based object detector for your project, particularly when deploying on lightweight hardware, requires consideration of trade-offs between accuracy, speed and computational efficiency.
In this talk, Elliott introduces the fundamental types of DNN-based object detectors. He covers models such as Faster R-CNN for high-accuracy applications and single-stage models such as YOLO and SSD for faster processing. He discusses lightweight architectures, including MobileNet, EfficientDet and vision transformers, which optimize object detection for resource-constrained environments. You will learn the trade-offs between object detection models for your computer vision applications, enabling informed choices for optimal performance and deployment.
See here for a PDF of the slides.
