基于改进RT-DETR的苹果病害检测算法OA

To address the challenges of strong background interference,difficulty in recognizing multi-scale lesions,and the need for lightweight deployment in apple disease detection within complex orchard en-vironments,an improved lightweight apple disease detection model named EGA-DETR was proposed based on RT-DETR.The model was enhanced in three key aspects.First,an RGLAN lightweight feature aggregation module was designed to reduce redundant computations and enhance feature representation through feature splitting and re-parameterized convolution.Second,a BiFPN-GLSA multi-scale feature fu-sion module was developed to improve the representation of lesions at various scales via bidirectional fea-ture transmission and a global-local self-attention mechanism.Third,an Inner-Shape-IoU loss function was introduced to improve model's localization accuracy for irregularly shaped lesion targets.Experimental results showed that EGA-DETR achieved 91.8%precision,88.9%recall,and 92.1%mAP50 on the apple disease dataset,with improvements of 3.5,3.5,and 1.5 percentage points over the baseline RT-DETR-18,respectively.Meanwhile,the number of model parameters was reduced to 11.8×106,which is 40.4%fewer than that of the baseline model,and the inference speed reached 120 frames per second.In summary,the EGA-DETR model achieved a favorable balance between detection accuracy and computational efficiency,providing robust technical support for accurate and real-time apple disease detection.