An Improved SSD Model for Saliency Object Detection
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摘要: 传统显著性目标检测方法常假设只有单个显著性目标，其效果依赖显著性阈值的选取，并不符合实际应用需求。近来利用目标检测方法得到显著性目标检测框成为一种新的解决思路。SSD模型可同时精确检测多个不同尺度的目标对象，但小尺寸目标检测精度不佳。为此，该文引入去卷积模块与注意力残差模块，构建了面向多显著性目标检测的DAR-SSD模型。实验结果表明，DAR-SSD检测精度显著高于SOD模型；相比原始SSD模型，在小尺度和多显著性目标情形下性能提升明显；相比MDF和DCL等深度学习框架下的方法，也体现了复杂背景情形下的良好检测性能。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.
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Key words:
- Object detection /
- Saliency object detection /
- Deconvolutional /
- Attention residual
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表 1 数据集构成(张)
数据集 MSO ECSSD DUT-OMRON HKU-IS 含显著性目标图像数 886 945 4893 3938 含单显著性目标图像数 611 807 4121 1276 含多显著性目标图像数 275 138 772 2662 含小尺度显著性目标图像数 446 323 3067 3020 含大尺度显著性目标图像数 440 622 1826 918 
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表 2 MAP方法下AP对照
AP-MAP MSO ECSSD DUT-OMRON HKU-IS 平均 SOD 0.7338 0.8152 0.5476 0.6938 0.6976 SSD 0.8229 0.8645 0.6698 0.8164 0.7934 DAR-SSD 0.8361 0.8766 0.6774 0.8317 0.8054
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表 3 NMS方法下AP对照
AP-NMS MSO ECSSD DUT-OMRON HKU-IS 平均 SOD 0.6104 0.7157 0.4409 0.5822 0.5873 SSD 0.8120 0.8619 0.6585 0.7974 0.7824 DAR-SSD 0.8387 0.8665 0.6737 0.8256 0.8011
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表 4 单显著性目标情形下AP对照
AP-one MSO ECSSD DUT-OMRON HKU-IS SSD+NMS 0.8823 0.8841 0.6908 0.8147 DAR-SSD+NMS 0.8844 0.8803 0.7052 0.8323 SSD+MAP 0.8881 0.8870 0.7076 0.8483 DAR-SSD+MAP 0.8877 0.8921 0.7127 0.8571
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表 7 大尺度显著性目标情形下AP对照
AP-large MSO ECSSD DUT-OMRON HKU-IS SSD+NMS 0.9016 0.8936 0.8190 0.8680 DAR-SSD+NMS 0.9017 0.8888 0.8253 0.8723 SSD+MAP 0.9019 0.8978 0.8330 0.8807 DAR-SSD+MAP 0.9019 0.8997 0.8306 0.8860
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表 5 多显著性目标情形下AP对照
AP-multi MSO ECSSD DUT-OMRON HKU-IS SSD+NMS 0.8207 0.7684 0.5920 0.8122 DAR-SSD+NMS 0.8583 0.8055 0.6071 0.8305 SSD+MAP 0.8453 0.7882 0.6073 0.8198 DAR-SSD+MAP 0.8616 0.8149 0.6137 0.8331
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表 6 小尺度显著性目标情形下AP对照
AP-small MSO ECSSD DUT-OMRON HKU-IS SSD+NMS 0.7984 0.7719 0.5734 0.7886 DAR-SSD+NMS 0.8288 0.7951 0.5980 0.8142 SSD+MAP 0.8044 0.7877 0.5786 0.8056 DAR-SSD+MAP 0.8310 0.8067 0.5948 0.8128
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表 8 多种显著性目标检测方法AP对照
数据集 RC GLGOV MDF DCL DAR-SSD+NMS DAR-SSD+MAP ECSSD 0.733 0.773 0.829 0.897 0.8665 0.8766 DUT-OMRON 0.503 0.539 0.649 0.675 0.6737 0.6774
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