| Autor: |
Li, Xinbin, Yu, Haifeng, Chen, Haiyang |
| Předmět: |
|
| Zdroj: |
Visual Computer; Feb2024, Vol. 40 Issue 2, p1299-1310, 12p |
| Abstrakt: |
Underwater object detection is a fascinating but challengeable subject in computer vision. Features are difficult to extract due to the color cast and blur of underwater images. Moreover, given the small scale of the underwater object, some details will be lost after several layers of convolution. Therefore, a multi-scale aggregation feature pyramid network is proposed to integrate multi-scale features and improve underwater object detection performance. Specifically, a lightweight and efficient network is used to extract the basic features. A special subnet is designed to improve the feature extraction capability of the backbone network to enrich the detailed features of small underwater objects. In addition, a multi-scale feature pyramid is proposed to enrich feature map. Each feature map enhances contextual information through a combination of up-sampling and down-sampling. The centerness strategy of the fully convolutional one-stage object detection head is improved by adding corner point regression to enhance the recall rate of small objects. Generalized intersection over union (GIoU) instead of IoU can better reflect the degree of coincidence between the actual box and the predicted box. Therefore, the regression loss is changed to GIoU loss. This paper evaluates the network on the underwater image dataset and obtains 78.90% mAP. Meanwhile, the experiment on the PASCAL VOC datasets is conducted and gets 84.3% mAP. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink