Deep learning

Physics

Pattern recognition (psychology)

Feature (linguistics)

Mathematics

Optics

Geodesy

Pyramid (geometry)

Mathematical analysis

Philosophy

Benchmark (surveying)

Artificial intelligence

Geography

Object detection

Pointwise

Computer science

Linguistics

Feature extraction

Computer vision

Telecommunications

Convolutional neural network

Detector

IEEE Access

Real-time scene parsing through object detection running on an embedded device is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we redesign a lightweight network without notably reducing detection accuracy. Based on the Darknet-53, we use depth separable convolutions and pointwise group convolutions to reduce the parameter size of the network. A feature extraction backbone network with a parameter size of only 16 percent of darknet-53 is constructed. Meanwhile, in order to compensate for the degradation of accuracy, we have added a Multi-Scale Feature Pyramid Network based on a simple U-shaped structure to improve the performance of multi-scale object detection, which called it Mini-YOLOv3. It has smaller model size and fewer trainable parameters and floating point operations (FLOPs) in comparison of YOLOv3. We evaluate Mini-YOLOv3 on MS-COCO benchmark dataset; The parameter size of Mini-YOLOv3 is only 23% of YOLOv3 and achieves comparable detection accuracy as YOLOv3 but only requires 1/2 detect time, Specifically, Mini-YOLOv3 achieves mAP-50 of 52.1 at speed of 67 fps.

Mini-YOLOv3: Real-Time Object Detector for Embedded Applications