Object detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding. Numerous approaches have been proposed to solve this problem, mainly inspired by methods of computer vision and deep learning. However, existing approaches always perform poorly for the detection of small, dense objects, and even fail to detect objects with random geometric transformations. In this study, we compare and analyse mainstream object detection algorithms and propose a multi-scaled deformable convolutional object detection network to deal with the challenges faced by current methods. Our analysis demonstrates a strong performance on par, or even better, than state of the art methods. We use deep convolutional networks to obtain multi-scaled features, and add deformable convolutional structures to overcome geometric transformations. We then fuse the multi-scaled features by up sampling, in order to implement the final object recognition and region regress. Experiments prove that our suggested framework improves the accuracy of detecting small target objects with geometric deformation, showing significant improvements in the trade-off between accuracy and speed.

目标检测方法旨在识别目标图像中的所有目标物体并确定类别和位置信息以实现机器视觉理解。人们提出了许多方法来解决这个问题，主要受到计算机视觉和深度学习方法的启发。然而，现有的方法对于检测小而密集的物体总是表现不佳，甚至无法检测具有随机几何变换的物体。在本研究中，我们比较和分析了主流的目标检测算法，并提出了一种多尺度可变形卷积目标检测网络来应对当前方法面临的挑战。我们的分析表明，其性能与最先进的方法相当，甚至更好。我们使用深度卷积网络来获得多尺度特征，并添加可变形卷积结构来克服几何变换。然后，我们通过上采样融合多尺度特征，以实现最终的对象识别和区域回归。实验证明，我们建议的框架提高了检测具有几何变形的小目标物体的准确性，显示出在准确性和速度之间的权衡方面的显着改进。