Objective

2

Humans fully understand a picture

often classify different images

and understand all the information in each image

including the location and concept of the object. This task is called object detection and is one of the basic research areas in computer vision. Object detection consists of different subtasks

such as pedestrian detection and skeleton detection. Pedestrian detection is a key link in object detection and one of the difficult tasks. This study mainly investigates pedestrian detection in traffic scenes

which is one of the most valuable topics in the field of pedestrian detection. Pedestrian detection in traffic scenes has always been a key technology for intelligent video surveillance technology

unmanned technology

intelligent transportation

and other issues. In recent years

this topic has been the research focus in academic and industrial circles. With the upsurge of artificial intelligence technology development

a large number of computer vision technologies are widely used. Multi-scale pedestrian detection has great research value because the development and application of pedestrian detection has complex real scenes and different pedestrian scales. Pedestrian detection is widely used in the field of automatic driving and video surveillance and is a hot research topic. Current pedestrian detection algorithms based on deep learning have false detection and miss detection problems in the case of low resolution and small pedestrian scale. A multi-scale pedestrian detection algorithm based on multi-layer features is proposed. The proposed convolutional neural network exhibits improved accuracy of pedestrian detection by a level

and there has been no small progress in practical applications. The academic enthusiasm brought about by deep learning has enabled scholars to make great progress and breakthroughs in pedestrian detection in complex scenes. Deep learning in the future will be a major boost for pedestrian detection.

Method

2

The deep residual network is mainly used in the multi-objective classification field. After analyzing the network

only the feature maps of the three stages are used and the residual unit and the full connection layer of the last stage are deleted. The deep residual network is mainly used to extract the feature maps of the three stages. The feature map extracted by the last layer is doubled using the characteristics of the three feature maps and then added by the nearest neighbor sampling method. The features with rich high-level semantic information and the features with rich low-level detail information are combined to improve the detection effect. The merged three-layer features are encoded into the region proposal network

and the proposal frames with pedestrians are obtained through Softmax classification for pedestrian detection. In this work

four experiments are designed

three of which are used to verify the validity of the proposed method. Results are compared with the mainstream algorithm results. Comparative experiments indicate that simple stratification does not improve the effect and the effect of multi-layer fusion is unsatisfactory. Therefore

the method of adjacent layer fusion is selected

and the result of multi-scale pedestrian detection is directly compared with that of the deepest network. The effect of adjacent layer fusion is better than the result. All experimental results are compared

and the fusion results of the adjacent layers are the best. The rate of missed detection is lower than that of the mainstream algorithm. The network is fully convolved and consists end-to-end training through random downsampling and backpropagation. Each image contains a number of candidate boxes for positive and negative samples. However

directly taking the optimized sample will easily lead to loss bias to the negative sample because the number of negative samples is larger than that of positive samples. This study takes an image to select 256 anchors and calculates its loss. The ratio of the positive and negative samples is 1:1. This article will randomly initialize all new layers in the network

and the standard of initialization is from the zero mean standard deviation. The value set is 0.01

and the weight is taken from Gaussian distribution. The other layers are initialized by classifying the pre-trained model

and the entire training process iterates through two epochs.

Result

2

On the Caltech pedestrian detection dataset and under the condition that each image false alarm rate (FPPI) is 10%

the loss rate of the proposed algorithm is only 57.88%

which is decreased by 3.07% compared with the loss of one of the best models

namely

MS-CNN (multi-scale convolutional neural network) (60.95%). This work also adopts comparative experiment. The overall loss rate of Ped-RPN is 64.55%

which is worse than that of the proposed algorithm. The loss rate of the layered and then detected method (Ped-muti-RPN) is 77.15%

which is better than that of Ped-RPN method. Ped-fused-RPN is a detection algorithm that combines multiple layers. The result is 61.32%

and the effect is better than the proposed algorithm.

Conclusion

2

Small-scale pedestrians have the disadvantage of blurred images

which make the detection effect extremely poor and affect the overall multi-scale detection. In order to solve the problem of the sharp decline of small-scale pedestrian detection

this paper proposes a method of integrating deep semantic information and shallow detail features so the features of all scales have rich semantic information. The deep features have high semantic information

and the receptive field is small. The shallow features have positional information

and the receptive field is more fused. The two features can enhance the deep features

which have rich target position information. The merged feature map has different levels of detail and semantic information and has a good effect on detecting pedestrians of different scales.