A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection
Xiamen University · Brunel University of London
Abstract
Object detection is a well-known task in the field of computer vision, especially the small target detection problem that has aroused great academic attention. In order to improve the detection performance of small objects, in this article, a novel enhanced multiscale feature fusion method is proposed, namely, the atrous spatial pyramid pooling-balanced-feature pyramid network (ABFPN). In particular, the atrous convolution operators with different dilation rates are employed to make full use of context information, where the skip connection is applied to achieve sufficient feature fusions. In addition, there is a balanced module to integrate and enhance features at different levels. The performance of the…
Citation impact
- FWCI
- 63.81
- Percentile
- 100%
- References
- 67
Authors
6Topics & keywords
- Computer science
- Object detection
- Pyramid (geometry)
- Artificial intelligence
- Feature (linguistics)
- Feature extraction
- Benchmark (surveying)
- Pattern recognition (psychology)