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

Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN