Semantic Segmentation Method Based on Residual and Multi-Scale Feature Fusion

Xiu, Chunbo; Su, Huan; Su, Xuemiao

doi:10.1109/ccdc49329.2020.9164415

Cited by 1 publication

(1 citation statement)

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“…Object detection is a basic task in computer vision, and its development has promoted other higher-level vision tasks, such as object tracking 1 and semantic segmentation. 2 Besides, object detection is widely used in autonomous driving, 3 medical object detection, 4 and other fields. 5 Currently, object detection algorithms are primarily divided into two categories: one-stage and two-stage algorithms.…”

Section: Introductionmentioning

confidence: 99%

MiniYOLO: A lightweight object detection algorithm that realizes the trade‐off between model size and detection accuracy

Liu

Zhang

et al. 2022

Int J of Intelligent Sys

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The object detection task is to locate and classify objects in an image. The current state‐of‐the‐art high‐accuracy object detection algorithms rely on complex networks and high computational cost. These algorithms have high requirements on the memory resource and computing capability of the deployed device, and are difficult to apply to mobile and embedded devices. Through the depthwise separable convolution and multiple efficient network structures, this paper designs a lightweight backbone network and two different multiscale feature fusion structures, and proposes a lightweight one‐stage object detection algorithm—MiniYOLO. With the model size of only 4.2 MB, MiniYOLO still maintains a high detection accuracy, realizing the trade‐off between the model size and detection accuracy. Experimental results on MS COCO 2017 data set show that compared to the state‐of‐the‐art PP‐YOLO‐tiny, MiniYOLO achieves higher mAP with the same model size. Compared with other lightweight object detection algorithms, MiniYOLO has certain advantages in detection accuracy or model size. The code associated with this paper can be downloaded from https://github.com/CaedmonLY/MiniYOLO.

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Section: Introductionmentioning

confidence: 99%