This study aims to enhance the detection performance of real-time monitoring deformers and explore their application potential in the field of sustainable development. We introduce two modules, deformable convolution and central difference convolution, to strengthen the capabilities of real-time monitoring deformers. Deformable convolution introduces learnable offsets into the convolution kernel, aligning the receptive fields with the actual object boundaries and extracting object features more accurately. Central difference convolution utilizes the central gradients between adjacent pixels to highlight local patterns, effectively improving the detection accuracy of object edges. To verify the effectiveness of the proposed convolution modules, we conducted experiments on the NEU-DET steel crack dataset and the COCO dataset. The results show that real-time monitoring deformers equipped with the deformable convolution module achieved a significant 1% increase in precision for medium-sized defects and a 0.1% improvement in recall for large-sized defects on the NEU-DET dataset. This indicates the promising application potential of the deformable convolution module in industrial inspection and quality control, contributing to improved production efficiency and reduced resource waste. On the COCO dataset, real-time monitoring deformers equipped with the central difference convolution module improved performance by 0.1% for medium-sized objects and enhanced AR by 0.5%. This demonstrates the excellent capability of the central difference convolution module in extracting fine-grained details and distinguishing objects from the background. In summary, both the deformable convolution and central difference convolution adapter modules enhance the object detection capability of real-time monitoring deformers in different application scenarios, providing new possibilities for their application in sustainable development-related fields, such as accurately detecting vehicles and pedestrians in intelligent transportation systems or early detection of cracks in infrastructure maintenance.