SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

Understand

Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.

• However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices.
• To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers.
• To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors.