Recently, significant improvements have been achieved for object detection algorithm by increasing the size of convolutional neural network (CNN) models, but the resulting increase of computational complexity poses an obstacle to practical applications. And some of the lightweight methods fail to consider the characteristics of object detection into and suffer a huge loss of accuracy. In this paper, we design a multi-scale feature lightweight network structure and specific convolution module for object detection based on depthwise separable convolution, which not only reduces the computational complexity but also improves the accuracy by using the specific position information in object detection. Furthermore, in order to improve the detection accuracy for small objects, we construct a multi-channel position-aware map and propose training based on knowledge distillation for object detection to train the lightweight model effectively. Last, we propose a training strategy based on a key-layer guiding structure to balance performance with training time. The experimental results show that on the COCO dataset that takes the state-of-the-art object detection algorithm, YOLOv3, as the baseline, our model size is compressed to 1/11 while accuracy drops by 7.4 mmAP, and the computational latency on the GPU and ARM platforms are reduced to 43.7% and 0.29%, respectively. Compared with the state-of-the-art lightweight object detection model, MNet V2 + SSDLite, the accuracy of our model increases by 3.5 mmAP while the inferencing time stays nearly the same. On the PASCAL VOC2007 dataset, the accuracy of our model increases by 5.2 mAP compared to the state-of-the-art lightweight algorithm based on knowledge distillation. Therefore, in terms of accuracy, parameter count, and real-time performance, our algorithm has better performance than lightweight algorithms based on knowledge distillation or depthwise separable convolution.

近年来，通过增加卷积神经网络（CNN）模型的大小，对目标检测算法进行了重大改进，但是由此导致的计算复杂性的增加给实际应用带来了障碍。并且一些轻量级方法没有考虑到物体检测的特征，并且遭受了巨大的准确性损失。本文设计了一种基于深度可分离卷积的多尺度特征轻量级网络结构和用于目标检测的特定卷积模块，不仅降低了计算复杂度，而且通过在目标检测中使用特定位置信息提高了准确性。此外，为了提高对小物体的检测精度，我们构造了一个多通道的位置感知地图，并提出了基于知识提炼的目标检测训练，以有效地训练轻量模型。最后，我们提出了一种基于关键层指导结构的培训策略，以平衡绩效与培训时间。实验结果表明，在以最先进的对象检测算法YOLOv3为基准的COCO数据集上，我们的模型大小被压缩为1/11，而精度下降了7.4 mmAP，并且计算延迟GPU和ARM平台分别降至43.7％和0.29％。与最新的轻型物体检测模型MNet V2 + SSDLite相比，我们的模型的精度提高了3.5 mmAP，而推理时间几乎保持不变。在PASCAL VOC2007数据集上，与基于知识蒸馏的最新轻量级算法相比，我们模型的准确性提高了5.2 mAP。因此，在准确性，参数计数和实时性能方面，我们的算法比基于知识蒸馏或深度可分离卷积的轻量级算法具有更好的性能。