Abstract
One-stage detectors are widely used in real-world computer vision applications nowadays due to their competitive accuracy and very fast speed. However, for high resolution (e.g., \(512 \times 512\)) input, most one-stage detectors run too slowly to process such images in real time. In this paper, we propose a novel one-stage detector called Dynamical Spatial Constraints for Object Detection (SCOD). We apply dynamical spatial constraints to address multiple detections of the same object and use two parallel classifiers to address the serious class imbalance. Experimental results show that SCOD makes a significant improvement in speed and achieves competitive accuracy on the challenging PASCAL VOC2007 and PASCAL VOC2012 benchmarks. On VOC2007 test, SCOD runs at 41 FPS with a mAP of 80.4%, which is \(2.2 {\times }\) faster than SSD that runs at 19 FPS with a mAP of 79.8%. On VOC2012 test, SCOD runs at 71 FPS with a mAP of 75.4%, which is \(1.8 {\times }\) faster than YOLOv2 that runs at 40 FPS with a mAP of 73.4%.
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Acknowledgments
This work is funded by the Natural Science Foundation of China (No. 61673204), State Grid Corporation of Science and Technology Projects (Funded No. SGLNXT00DKJS1700166).
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Zhang, KJ., Guo, CH., Niu, ZH., Liu, LF., Yang, YB. (2019). SCOD: Dynamical Spatial Constraints for Object Detection. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_2
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