  This paper introduces a Deep Learning Convolutional Neural Network model
based on Faster-RCNN for motorcycle detection and classification on urban
environments. The model is evaluated in occluded scenarios where more than 60%
of the vehicles present a degree of occlusion. For training and evaluation, we
introduce a new dataset of 7500 annotated images, captured under real traffic
scenes, using a drone mounted camera. Several tests were carried out to design
the network, achieving promising results of 75% in average precision (AP), even
with the high number of occluded motorbikes, the low angle of capture and the
moving camera. The model is also evaluated on low occlusions datasets, reaching
results of up to 92% in AP.

Computer Vision and Pattern Recognition