Abstract: Pedestrian detectors require the algorithm to be fast and accurate. Although pedestrian detectors based on deep
convolutional neural networks (DCNN) have high detection accuracy, such detectors require higher capacity of
calculation. Therefore, such pedestrian detectors cannot be deployed well on lightweight systems, such as mobile devices,
embedded devices, and autonomous driving systems. Considering these problems, a lightweight and effective pedestrian detector (EPDNet) was proposed, which can better balance speed and accuracy. First, the shallow convolution layers of the backbone network employed depthwise separable convolution to compress the parameters of model, and the deeper convolution layers utilized standard convolution to extract high-level semantic features. In addition, in order to further improve the performance of the model, the backbone network adopted a feature fusion method to enhance the expression ability of its output features. Through comparative experiments, EPDNet has shown superior performance on two
challenging pedestrian datasets, Caltech and CityPersons. Compared with the benchmark model, EPDNet has obtained a
better trade-off between speed and accuracy, improving the speed and accuracy of EPDNet at the same time.

Key words:  standard convolution, depthwise separable convolution, feature fusion, lightweight, pedestrian detection