Much research has been done in implementing deep learning architectures in detection and recognition tasks. Current work in auto-encoders and generative adversarial networks suggest the ability to recreate scenes based on previously trained data. It can be assumed that with the ability to recreate information is the ability to differentiate information. We propose a convolutional auto-encoder for both recreating information of the scene and for detection of vehicles from within the scene. In essence, the auto-encoder creates a low-dimensional representation of the data projected in a latent space, which can also be used for classification. The convolutional neural network is based on the concept of receptive fields created by the network, which are part of the detection process. The proposed architecture includes a discriminator network connected in the latent space, which is trained for the detection of vehicles. Through work in multi-task learning, it is advantageous to learn multiple representations of the data from different tasks to help improve task performance. To test and evaluated the network, we use standard aerial vehicle data sets, like Vehicle Detection in Aerial Imagery (VEDAI) and Columbus Large Image Format (CLIF). We observe that the neural network is able to create features representative of the data and is able to classify the imagery into vehicle and non-vehicle regions.
Theus Aspiras, Ruixu Liu, and Vijayan K. Asari, "Convolutional auto-encoder for vehicle detection in aerial imagery (Conference Presentation)," Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 109950D (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 13 May 2019); https://doi.org/10.1117/12.2520253.6036141706001.
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