SSD real-time illegal parking detection based on contextual information transmission

Abstract

With the improvement of the national economic level, the number of vehicles is still increasing year by year. According to the statistics of National Bureau of Statics, the number is approximately up to 327 million in China by the end of 2018, which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing. Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision, which may miss detection and cost much manpower. Due to the rapidly developing deep learning sweeping the world in recent years, object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision. Thus, an improved Single Shot MultiBox Detector (SSD) based on deep learning is proposed in our study, we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer. Finally, we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5% than the baseline SSD without extra training cost. Meanwhile, we designed an illegal parking vehicle detection method by the improved SSD, reaching a high precision up to 97.3% and achieving a speed of 40FPS, superior to most of vehicle detection methods, will make contributions to relieving the negative impact of illegal parking.