Alpha-SGANet: A multi-attention-scale feature pyramid network combined with lightweight network based on Alpha-IoU loss.

ABSTRACT: The design of deep convolutional neural networks has resulted in significant advances and successes in the field of object detection. However, despite these achievements, the high computational and memory costs of such object detection networks on the edge or in mobile scenarios are one of the most significant barriers to their broad adoption. To solve this problem, this paper introduces an improved lightweight real-time convolutional neural network based on YOLOv5, called Alpha-SGANet: A multi-attention-scale feature pyramid network combined with a lightweight network based on Alpha-IoU loss. Firstly, we add one more prediction head to detect different-scale objects, design a lightweight and efficient feature extraction network using ShuffleNetV2 in the backbone, and reduce information lo

SUBMITTER: Li H 

PROVIDER: S-EPMC9612525 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature