The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Inception Single Shot MultiBox Detector for object detection
Abstract
In the current object detection field, one of the fastest algorithms is the Single Shot Multi-Box Detector (SSD), which uses a single convolutional neural network to detect the object in an image. Although SSD is fast, there is a big gap compared with the state-of-the-art on mAP. In this paper, we propose a method to improve SSD algorithm to increase its classification accuracy without affecting its speed. We adopt the Inception block to replace the extra layers in SSD, and call this method Inception SSD (I-SSD). The proposed network can catch more information without increasing the complexity. In addition, we use the batch-normalization (BN) and the residual structure in our I-SSD network architecture. Besides, we propose an improved non-maximum suppression method to overcome its deficiency on the expression ability of the model. The proposed I-SSD algorithm achieves 78.6% mAP on the Pascal VOC2007 test, which outperforms SSD algorithm while maintaining its time performance. We also construct an Outdoor Object Detection (OOD) dataset to testify the effectiveness of the proposed I-SSD on the platform of unmanned vehicles.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Object Detection Networks on Convolutional Feature Maps
Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1504.06066 · Cited by 454
Cited by
Weighted boxes fusion: Ensembling boxes from different object detection models
Solovyev, R., Wang, W., & Gabruseva, T. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104117 · Cited by 543
Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3153997 · Cited by 523
Deep learning-based cost-effective and responsive robot for autism treatment
Singh, A., Raj, K., Kumar, T., Verma, S., & Roy, A. (2023) · Drones · doi:10.3390/drones7020081 · Cited by 154
3D Object Detection from Images for Autonomous Driving: A Survey
Ma, X., Ouyang, W., Simonelli, A., & Ricci, E. (2022) · arXiv · doi:10.48550/arxiv.2202.02980 · Cited by 147
A Review: Object Detection Models
Shetty, A. K., Saha, I., Sanghvi, R. M., Save, S. A., & Patel, Y. J. (2021) · 2021 6th International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct51068.2021.9417895 · Cited by 67
Metadata
- Authors
- Ning, C., Zhou, H., Song, Y., & Tang, J.
- Venue
- 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 549-554
- OpenAlex
- W2753041493
- Cited by
- 194
- References
- 5
- FWCI
- 3.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ning, C., Zhou, H., Song, Y., & Tang, J. (2017). Inception Single Shot MultiBox Detector for object detection. 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). https://doi.org/10.1109/icmew.2017.8026312
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