Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

移动平台上的实时目标检测是一项至关重要但具有挑战性的计算机视觉任务。然而，人们普遍认识到，虽然轻量级物体检测器具有较高的检测速度，但检测精度相对较低。为了提高检测精度，在视觉认知任务中提取完整的多尺度图像特征是有益的。非对称卷积有一个有用的品质，即它们具有不同的长宽比，可以用于精确物体的图像特征，特别是具有多尺度特征的物体。在本文中，我们并行利用三种不同的非对称卷积，并提出一种新的多尺度非对称卷积单元，即 MAC 块，以增强 CNN 的多尺度表示能力。此外，MAC块可以通过将可学习的加权参数分配给三个不同的非对称卷积分支来自适应地合并不同尺度的特征。所提出的 MAC 块可以插入到 ResNet-50 等最先进的主干网络中，以形成新的多尺度目标检测器主干网络。为了评估 MAC 块的性能，我们在 CIFAR-100、PASCAL VOC 2007、PASCAL VOC 2012 和 MS COCO 2014 数据集上进行了实验。实验结果表明，在保证较快的检测速度的同时，可以大大提高检测精度。