Network layer

Linguistics

Pedestrian

Engineering

Philosophy

Transport engineering

Materials science

Layer (electronics)

Real-time computing

Computer science

Pedestrian detection

Computer network

Pattern recognition (psychology)

Feature (linguistics)

Artificial intelligence

Composite material

Backbone network

Computer vision

After going through the deep network, there will be some loss of pedestrian information, which will cause the disappearance of gradients, causing inaccurate pedestrian detection. This paper improves the network structure of YOLO algorithm and proposes a new network structure YOLO-R. First, three Passthrough layers were added to the original YOLO network. The Passthrough layer consists of the Route layer and the Reorg layer. Its role is to connect the shallow layer pedestrian features to the deep layer pedestrian features and link the high and low resolution pedestrian features. The role of the Route layer is to pass the pedestrian characteristic information of the specified layer to the current layer, and then use the Reorg layer to reorganize the feature map so that the currently-introduced Route layer feature can be matched with the feature map of the next layer. The three Passthrough layers added in this algorithm can well transfer the network's shallow pedestrian fine-grained features to the deep network, enabling the network to better learn shallow pedestrian feature information. This paper also changes the layer number of the Passthrough layer connection in the original YOLO algorithm from Layer 16 to Layer 12 to increase the ability of the network to extract the information of the shallow pedestrian features. The improvement was tested on the INRIA pedestrian dataset. The experimental results show that this method can effectively improve the detection accuracy of pedestrians, while reducing the false detection rate and the missed detection rate, and the detection speed can reach 25 frames per second.

Pedestrian Detection Based on YOLO Network Model