This paper proposes an enhanced fine-grained target detection and recognition algorithm named YOLOX-DCSA specifically tailored for aircraft remote sensing images. Traditional fine-grained detection techniques struggle with small inter-category differences, challenging feature extraction, and low recognition accuracy in remote sensing images. To address these issues, YOLOX-DCSA integrates a DCSA attention module, which combines channel and spatial attention mechanisms with dilated convolution to expand the receptive field and improve discrimination among various aircraft categories. Additionally, depthwise separable convolution is employed in the feature pyramid network to reduce model parameters and enhance computational efficiency. A BD-CSP module is also designed to further enhance the receptive field and improve feature extraction capabilities for fine-grained targets. Experimental results demonstrate that YOLOX-DCSA outperforms existing mainstream target detection and recognition algorithms in terms of recognition accuracy, parameter size, and running time, thereby validating its effectiveness in identifying different types of aircraft targets in remote sensing images. The open-source code will be released at https://github.com/DEIRDRE1414/YOLOX-DCSA.git.