State of Deep Learning for Object Detection - You Should Consider CenterNets! | Victor Dibia
YOLOv4: Optimal Speed and Accuracy of Object Detection – arXiv Vanity
GitHub - PINTO0309/MobileNet-SSD-RealSense: [High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + ...
Object Detection Part 4: Fast Detection Models | Lil'Log
Real-Time Vehicle Detection- MobileNet SSD and Xailient
Neural architecture of SSD-MobileNet V2. | Download Scientific Diagram
SSD MobileNet functional block diagram. | Download Scientific Diagram
Object Detection using SSD Mobilenet V2 | by Vidish Mehta | Medium
Object Detection Using YOLO And Mobilenet SSD Computer Vision -
Real-Time Vehicle Detection- MobileNet SSD and Xailient
Sensors | Free Full-Text | Detection of Lower Body for AGV Based on SSD Algorithm with ResNet
Applied Sciences | Free Full-Text | Lightweight Deep Learning for Road Environment Recognition
Mobile object detector with TensorFlow Lite | by Quantum | DataDrivenInvestor
Real-Time Vehicle Detection with MobileNet SSD and Xailient - Edge AI and Vision Alliance
Omni-benchmarking Object Detection | by Dr Anton Lokhmotov | Towards Data Science
a) represents the detection results of the improved network on... | Download Scientific Diagram
tensorflow - SSD-mobilenet object detection algorithm not converging - Stack Overflow
Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems | Semantic Scholar
Object Detection with MobileNet-SSD slower than mentioned speed - Jetson Nano - NVIDIA Developer Forums
Comparison of YOLOv3, YOLOv5s and MobileNet-SSD V2 for Real-Time Mask Detection
MobileNetV2: The Next Generation of On-Device Computer Vision Networks – Google AI Blog
Object detection in real time based on improved single shot multi-box detector algorithm | EURASIP Journal on Wireless Communications and Networking | Full Text
Optimized MobileNet + SSD: a real-time pedestrian detection on a low-end edge device | SpringerLink
