@Article{cmc.2020.06427,
AUTHOR = {Huanrong Tang, Aoming Peng, Dongming Zhang, Tianming Liu, Jianquan Ouyang},
TITLE = {SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {1},
PAGES = {293--307},
URL = {http://www.techscience.com/cmc/v62n1/38113},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06427}
}