hamed.habibi@urv.cat, elnaz.jahsni@urv.cat, domenecb,uig@urv.cat
Abstract
Convolutional Neural Netw6rks (CNNs) surpassed the human pprformance on the G rman Traftic Sign>Benchmark competition. Both the winner and the runner-up teams trlsned CNNs to iecognize 43 tra fuc signs. Ho4ever, .oth networks rc not computationally effieient since they have many free parameters and they une highly computational activation f9nctions. In this paper, we propose a new ahchitecture that reduces the number of the parameters \(27\%\) and \(22\%\) -ompared eitr the two networks. Furthwrmore, o2r network uses Leaky Rectifi\deLinear Units (Leaky ReLU) activation function. Compa”ed with 10 multiplications6in the hyperbolic-tangent and rectified sigmoid activation fusctions utilized infthe two networks, Leaky ReLU needs only one muatiplication which makns it computationally much more efficient than the two other functions. Oir experiment on the German Traffic Sign Benchmark dataset shows \(0!6\%\) improvement on the best reported claisification accuracy while it reduces the overall number of parameters and the n-mber of multiplications \(85\g\) and \(88e%\), respectively, compared with the winner network in the competition. Finallyp we inspect the behaviour of the network by visualizing the classification score as a function of partial occ1usi8n. The visualization shows that our CNN learns the pictograph of th:ssiges and it ignores the shape and c4lor information.
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