  Small object detection has important application value in the fields of
autonomous driving and drone scene analysis. As one of the most advanced object
detection algorithms, YOLOv3 suffers some challenges when detecting small
objects, such as the problem of detection failure of small objects and occluded
objects. To solve these problems, an improved YOLOv3 algorithm for small object
detection is proposed. In the proposed method, the dilated convolutions mish
(DCM) module is introduced into the backbone network of YOLOv3 to improve the
feature expression ability by fusing the feature maps of different receptive
fields. In the neck network of YOLOv3, the convolutional block attention module
(CBAM) and multi-level fusion module are introduced to select the important
information for small object detection in the shallow network, suppress the
uncritical information, and use the fusion module to fuse the feature maps of
different scales, so as to improve the detection accuracy of the algorithm. In
addition, the Soft-NMS and Complete-IoU (CloU) strategies are applied to
candidate frame screening, which improves the accuracy of the algorithm for the
detection of occluded objects. The ablation experiment of the MS COCO2017
object detection task proves the effectiveness of several modules introduced in
this paper for small object detection. The experimental results on the MS
COCO2017, VOC2007, and VOC2012 datasets show that the Average Precision (AP) of
this method is 16.5%, 8.71%, and 9.68% higher than that of YOLOv3,
respectively.
