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/.

中文翻译：

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MiniYOLO：实现模型尺寸和检测精度折衷的轻量级物体检测算法

目标检测任务是对图像中的目标进行定位和分类。当前最先进的高精度目标检测算法依赖于复杂的网络和高计算成本。这些算法对部署设备的内存资源和计算能力要求较高，难以应用于移动和嵌入式设备。通过depthwise separable convolution和多个高效的网络结构，本文设计了一个轻量级的骨干网络和两种不同的多尺度特征融合结构，并提出了一种轻量级的单阶段目标检测算法——MiniYOLO。在模型大小仅为4.2MB的情况下，MiniYOLO仍然保持了较高的检测精度，实现了模型大小与检测精度的折衷。在 MS COCO 2017 数据集上的实验结果表明，与最先进的 PP-YOLO-tiny 相比，MiniYOLO 在相同的模型尺寸下实现了更高的 mAP。与其他轻量级目标检测算法相比，MiniYOLO无论是在检测精度上还是在模型尺寸上都具有一定的优势。与本文相关的代码可以从 https://github.com/CaedmonLY/MiniYOLO/ 下载。