To address the challenges of complex backgrounds, small targets, and dense distributions in images, an improved method called DES-YOLO is proposed. By introducing the deformable attention module (DAM), the network can dynamically focus on key regions, improving object recognition and localization accuracy. The efficient intersection over union (EIoU) loss function is employed to reduce the impact of low-quality samples, enhancing the model's generalization ability and detection accuracy. A shallow feature map layer of 160 pixel×160 pixel is added to the network head to strengthen small target feature extraction. A stepwise training strategy is also adopted to further improve model performance. Experimental results show that the mAP@50 of the model increased by 1.4% on the remote sensing dataset and by 1.7% on the textile dataset, demonstrating the broad applicability and effectiveness of DES-YOLO.