农作物图像在进行目标检测时，由于作物种植较密集、成像质量不佳等原因严重影响目标检测算法的检测精度。针对存在的问题，提出一种基于YOLOv3的改进算法优化在农作物目标检测的检测性能：对YOLOv3的主干特征提取网络进行优化，利用原网络中输出的4倍降采样特征图对目标进行检测，并且在算法原网络残差块的基础上增加残差单元，以检测目标较小的农作物位置信息；提出高斯衰减函数，对图像中高度重叠的农作物候选框的衰减较强，在有效抑制冗余框的同时也可以有效地降低漏检率；对回归损失函数进行优化改进，用CIOU Loss作为损失函数，使得目标检测过程中最终的目标定位更加精确。将改进的 YOLOv3算法和原 YOLOv3 算法、Faster R-CNN 算法在实拍的玉米作物图像数据集上进行对比实验，结果表明改进后的YOLOv3算法能有效检测农作物小目标，算法检测的平均准确率均值和检测速度都有明显的提升。

When detecting targets in crop images, the detection accuracy of target detection algorithms can be seriously affected due to factors such as dense crop planting and poor imaging quality. In order to optimize the detection performance of crop object detection in YOLOv3, an improved algorithm based on YOLOv3 is proposed. Firstly, the backbone feature extraction network of YOLOv3 is optimized by utilizing the downsampling feature maps outputted by the original network to detect targets, and residual units are added on the basis of the residual blocks in the original network to detect the position information of small crop objects. Moreover, a Gaussian decay function is introduced to attenuate highly overlapping crop candidate boxes in the image, effectively suppressing redundant boxes and reducing false negative rate. Furthermore, the regression loss function is optimized by using CIOU Loss, making the final object localization more accurate during the object detection process. To evaluate the improved YOLOv3 algorithm, a comparative experiment is conducted on a real-world dataset of maize crop images, comparing it with the original YOLOv3 algorithm and the Faster R-CNN algorithm. The results demonstrate that the improved YOLOv3 algorithm can effectively detect small crop targets, exhibiting significantly improved mean average precision and detection speed.