An Improved SSD Model for Saliency Object Detection

Chunyan YU; Xiaodan XU; Shijun ZHONG

doi:10.11999/JEIT180118

Volume 40 Issue 11

Oct. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(11): 2554-2561

Chunyan YU, Xiaodan XU, Shijun ZHONG. An Improved SSD Model for Saliency Object Detection[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2554-2561. doi: 10.11999/JEIT180118

Citation:

Chunyan YU, Xiaodan XU, Shijun ZHONG. An Improved SSD Model for Saliency Object Detection[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2554-2561. doi: 10.11999/JEIT180118

Chunyan YU, Xiaodan XU, Shijun ZHONG. An Improved SSD Model for Saliency Object Detection[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2554-2561. doi: 10.11999/JEIT180118

Citation:

Chunyan YU, Xiaodan XU, Shijun ZHONG. An Improved SSD Model for Saliency Object Detection[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2554-2561. doi: 10.11999/JEIT180118

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An Improved SSD Model for Saliency Object Detection

doi: 10.11999/JEIT180118

College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China

Funds: The Major Project in Industry-university Cooperation of Fujian Province (2016H6010), The Natural Science Foundation of Fujian Province (2015J01420), The Guiding Found of Fujian Province (2016Y0060), The Health-Education Joint Project of Fujian Province (WKJ2016-2-26)

Received Date: 2018-01-26
Rev Recd Date: 2018-07-17

Available Online: 2018-07-27

Publish Date: 2018-11-01

Abstract

Abstract

Traditional saliency object detection methods, assuming that there is only one salient object, is not conductive to practical application. Their effects are dependent on saliency threshold. Object detection model provides a kind of new solutions. SSD can accurately detect multi-objects with different scales simultaneously, except for small objects. To overcome this drawback, this paper presents a new multi- saliency objects detection model, DAR-SSD, appending a deconvolution module embedded with an attention residual module. Experiments show that DAR-SSD achieves a higher detection accuracy than SOD. Also, it improves detection performance for multi- saliency objects on small scales, compared with original SSD, and it has an advantage over complicated background, compared with MDF and DCL, which also are deep model based methods.
- Object detection,
- Saliency object detection,
- Deconvolutional,
- Attention residual

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References(19)

References

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