High-Speed Car Detection Using ResNet-Based Recurrent Rolling Convolution

Car detection is a crucial issue in self-driving cars. Numerous in-traffic car detection models have been proposed, each of which exhibits its own strengths and weaknesses; the high detection speeds of some models are not accompanied by high precision, while the precision of other models is shadowed by insufficient speeds. Our main goal in this paper is to introduce a model that utilizes the Recurrent Rolling Convolution (RRC). The model gives promising results on detection speed and precision, thereby mitigating the weaknesses of previously proposed models, which is exhibited in our extensive experiment.