Performance analysis of mAlexnet by training option and activation function tuning on parking images

Abstract

Information on the availability of parking spaces is one of the main needs in urban areas. This information can minimize the impact of vehicle growth, including exhaust gas emissions, traffic jams, and fuel use. In general, the detection of parking space availability can be done in two ways, namely the sensor system and computer vision. Computer vision monitoring of parking spaces is more promising for use in the future. A single-camera can monitor multiple parking spaces by making use of computer vision. CNN is a technique for parking space classification. CNN’s pre-trained dedicated to parking space classification is mAlexnet. mAlexnet can classify parking spaces well, but not perfect. in this paper, we try to observe and improve the performance of mAlexnet. We tried training options and activation function tuning. From the results of testing the combination of SGDM training option and the LeakyReLu activation function, the performance of mAlexnet improves.