Small object detection is always a hot research direction in the field of computer vision, and widely used in traffic, production, industry, and other fields. A neural network for detecting small objects based on original Cascade RCNN is proposed. First, we modify the traditional feature pyramid network and introduce multi-branch dilated convolutions to enhance the feature information of the small target. Second, the feature extraction networks in original Cascade RCNN are replaced with multi-layer deformable convolution networks, which can better adapt to the geometric variations of detection target and huge size span of the objects in the same scene. Finally, Soft non-maximum suppression is also integrated into the network to avoid problems in dense object detection. To verify the practicability of our proposed network, we apply it to the garbage detection system. The experimental results show that compared with original Cascade RCNN and other commonly used target detection networks, our proposed method, with a recall rate >0.98 as well as mean average precision up to 0.964, performs better not only in small object detection but also in industrial applications.

中文翻译：

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使用深度卷积网络的小物体检测：应用于垃圾检测系统

小物体检测一直是计算机视觉领域的热门研究方向，广泛应用于交通、生产、工业等领域。提出了一种基于原始Cascade RCNN的小物体检测神经网络。首先，我们修改传统的特征金字塔网络，引入多分支扩张卷积来增强小目标的特征信息。其次，将原始Cascade RCNN中的特征提取网络替换为多层可变形卷积网络，可以更好地适应检测目标的几何变化和同一场景中物体的巨大尺寸跨度。最后，软非极大值抑制也被集成到网络中，以避免在密集物体检测中出现问题。为了验证我们提出的网络的实用性，我们将其应用于垃圾检测系统。实验结果表明，与原始的 Cascade RCNN 和其他常用目标检测网络相比，我们提出的方法，召回率 >0.98，平均精度高达 0.964，不仅在小物体检测方面表现更好，在工业领域也表现出色。应用程序。