In this paper, an improved algorithm based on Yolo V3 is proposed, which can effectively improve the accuracy of small target detection. First of all, the feature map acquisition network is improved. The image double-segmentation and bilinear upsampling network are used to replace the 2-step downsampling convolution network in the original network architecture, and the feature values of large and small objects are amplified. Secondly, a size recognition module is added to the input image to reduce the loss of morpheme features caused by no-feature value filling and enhance the recognition ability of small objects. Thirdly, in order to avoid the gradient fading of the network, the residual network element of the output network layer is added to enhance the feature channel of small object detection. Compared with Yolo V3, our algorithm improves the detection accuracy of small objects from 82.4 to 88.5%, the recall rate from 84.6 to 91.3%, and the average accuracy from 95.5 to 97.3%, respectively.

提出了一种基于Yolo V3的改进算法，可以有效提高小目标检测的准确性。首先，改进了特征图获取网络。图像双分割和双线性上采样网络被用来代替原始网络体系结构中的两步下采样卷积网络，并且放大了大小物体的特征值。其次，在输入图像上增加尺寸识别模块，以减少因无特征值填充而造成的语素特征损失，增强对小物体的识别能力。第三，为了避免网络的梯度衰落，增加了输出网络层的剩余网络元素，以增强小物体检测的特征通道。与Yolo V3相比，