Embedded and mobile smart devices face problems related to limited computing power and excessive power consumption. To address these problems, we propose Mixed YOLOv3-LITE, a lightweight real-time object detection network that can be used with non-graphics processing unit (GPU) and mobile devices. Based on YOLO-LITE as the backbone network, Mixed YOLOv3-LITE supplements residual block (ResBlocks) and parallel high-to-low resolution subnetworks, fully utilizes shallow network characteristics while increasing network depth, and uses a "shallow and narrow" convolution layer to build a detector, thereby achieving an optimal balance between detection precision and speed when used with non-GPU based computers and portable terminal devices. The experimental results obtained in this study reveal that the size of the proposed Mixed YOLOv3-LITE network model is 20.5 MB, which is 91.70%, 38.07%, and 74.25% smaller than YOLOv3, tiny-YOLOv3, and SlimYOLOv3-spp3-50, respectively. The mean average precision (mAP) achieved using the PASCAL VOC 2007 dataset is 48.25%, which is 14.48% higher than that of YOLO-LITE. When the VisDrone 2018-Det dataset is used, the mAP achieved with the Mixed YOLOv3-LITE network model is 28.50%, which is 18.50% and 2.70% higher than tiny-YOLOv3 and SlimYOLOv3-spp3-50, respectively. The results prove that Mixed YOLOv3-LITE can achieve higher efficiency and better performance on mobile terminals and other devices.

中文翻译：

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混合YOLOv3-LITE：一种轻量级的实时对象检测方法。

嵌入式和移动智能设备面临与有限的计算能力和过多的功耗有关的问题。为了解决这些问题，我们提出了混合YOLOv3-LITE，这是一种轻量级的实时对象检测网络，可以与非图形处理单元（GPU）和移动设备一起使用。基于YOLO-LITE作为骨干网络，混合YOLOv3-LITE补充了残差块（ResBlocks）和并行的高至低分辨率子网，在增加网络深度的同时充分利用了浅层网络特性，并使用了“浅而窄”的卷积层以构建检测器，从而在与基于非GPU的计算机和便携式终端设备一起使用时，在检测精度和速度之间达到最佳平衡。在这项研究中获得的实验结果表明，提出的混合YOLOv3-LITE网络模型的大小为20.5 MB，分别比YOLOv3，tiny-YOLOv3和SlimYOLOv3-spp3-50小91.70％，38.07％和74.25％，分别。使用PASCAL VOC 2007数据集获得的平均平均精度（mAP）为48.25％，比YOLO-LITE高出14.48％。使用VisDrone 2018-Det数据集时，使用混合YOLOv3-LITE网络模型实现的mAP为28.50％，分别比tiny-YOLOv3和SlimYOLOv3-spp3-50高18.50％和2.70％。结果证明，混合YOLOv3-LITE可以在移动终端和其他设备上实现更高的效率和更好的性能。使用PASCAL VOC 2007数据集获得的平均平均精度（mAP）为48.25％，比YOLO-LITE高出14.48％。使用VisDrone 2018-Det数据集时，使用混合YOLOv3-LITE网络模型实现的mAP为28.50％，分别比tiny-YOLOv3和SlimYOLOv3-spp3-50高18.50％和2.70％。结果证明，混合YOLOv3-LITE可以在移动终端和其他设备上实现更高的效率和更好的性能。使用PASCAL VOC 2007数据集获得的平均平均精度（mAP）为48.25％，比YOLO-LITE高出14.48％。使用VisDrone 2018-Det数据集时，使用混合YOLOv3-LITE网络模型实现的mAP为28.50％，分别比tiny-YOLOv3和SlimYOLOv3-spp3-50高18.50％和2.70％。结果证明，混合YOLOv3-LITE可以在移动终端和其他设备上实现更高的效率和更好的性能。