Academic Journal of Computing & Information Science, 2023, 6(6); doi: 10.25236/AJCIS.2023.060611.
Wen Zhou, Yan Gou, Langlang Chen, Tian Shi, Zisu Yuan
School of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, China
SSD is a single-stage target detection algorithm, which performs feature extraction by convolutional neural network and takes different feature layers for detection output, so SSD is a multi-scale detection method. In the feature layer to be detected, a 3*3 convolution is directly used to perform the transformation of the channels. ssd uses an anchor strategy with pre-defined anchors of different aspect ratios, and each output feature layer predicts multiple detection frames (4 or 6) based on the anchor. A multi-scale detection approach is used, where a shallow layer is used to detect small targets and a deep layer is used to detect large targets. yolov5 is a single-stage target detection algorithm, which adds some new and improved ideas to yolov4, resulting in a significant performance improvement in both speed and accuracy. We conduct algorithm experiments with SSD and YOLOv5, and analyze the experiments to obtain better improvement ideas for small target algorithm.
SSD; YOLOv5; target detection algorithm
Wen Zhou, Yan Gou, Langlang Chen, Tian Shi, Zisu Yuan. Analysis of small target detection algorithm based on SSD and YOLOv5. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 6: 73-79. https://doi.org/10.25236/AJCIS.2023.060611.
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