矿山开采引起的地表裂缝对煤矿安全生产造成严重影响，利用无人机影像识别采煤沉陷区裂缝发育特征具有实用价值。提出了一种改进的YOLOv7采动裂缝智能化识别模型，通过在模型主干网络中添加GAM全局注意力机制，减少图像卷积过程中特征信息的丢失；同时，引入深度可分离卷积代替主干网络中的普通卷积，使得图像特征图尺寸减半，而卷积过程中并不会造成特征缺失；进一步采用GIOU_Loss对网络边界框损失函数进行改进，提升了模型的小目标识别能力；使用LabelImg图像标注软件制作包含2143张$ 5;472times 3;648 $ pixels的采动裂缝识别训练数据集，对沉陷区无人机影像进行初处理构建DOM后，使用$ 640times 640 $的滑动窗口裁剪DOM，对裁剪后的DOM进行特征识别。结果表明：改进后的YOLOv7模型对于密集、细小、植被覆盖3类裂缝图像的识别效果和漏检情况得到明显改善，平均准确度(mAP)达到0.84。

The surface cracks caused by mining have a significant impact on the safety production of coal mines. Utilizing unmanned aerial vehicle (UAV) image recognition to identify the development characteristics of cracks in coal mining subsidence areas holds practical value. In this paper, an improved YOLOv7 model for intelligent recognition of mining-induced cracks is proposed. It incorporates the GAM global attention mechanism into the backbone network of the model to reduce the loss of feature information during the image convolution process. Additionally, depth-wise separable convolutions are introduced to replace the ordinary convolutions in the backbone network, which reduces the size of the image feature map without causing feature loss during the convolution process. Furthermore, GIOU_Loss is further used to improve the network boundary frame loss function, which improves the small target identification ability of the model. For the experiments, a training dataset for mining-induced crack recognition was created using the LabelImg image annotation software, containing 2 143 images with a resolution of 5 472×3 648 pixels. After initial preprocessing of the UAV images in the subsidence area to construct a digital orthophoto model (DOM), the DOM was further cropped using a sliding window of size 640×640 for feature recognition. The experimental results demonstrate that the improved YOLOv7 model exhibits significant improvements in the recognition performance and missed detections of dense, small-sized, and vegetation-covered crack images. The average precision (mAP) reaches 0.84.