Sensors, Vol. 23, Pages 1984: Inception Convolution and Feature Fusion for Person Search
Sensors, Vol. 23, Pages 1984: Inception Convolution and Feature Fusion for Person Search
Sensors doi: 10.3390/s23041984
Authors:
Huan Ouyang
Jiexian Zeng
Lu Leng
With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), w...
Source: Sensors - Category: Biotechnology Authors: Huan Ouyang Jiexian Zeng Lu Leng Tags: Article Source Type: research