Lightweight and real-time object detection model on edge devices with model quantization

Environment perception is of vital importance in autonomous driving as it serves as autonomous car's "eye" to perceive the surrounding environment. We propose a solution to achieve real-time environment perception on resource constrained edge devices like mobile phones, even though they have limited computation resources. We used float-to-int quantization based on TensorFlow Lite to quantilize the SSD model. TensorFlow Lite achieves accuracy lossless quantization with quantization-aware training by inserting fake quant operator during training, which imitates the loss introduced by quantization in feedforward training. Our experiment shows that superiority of our proposed method, which is highly speed, light weight and relatively high accuracy. (paper)