A robotic arm equipped with a vision system can significantly improve production efficiency. However, due to the complexity of the robotic arm’s working environment, the useful information about the objects recognized by the visual system is significantly reduced. To solve this problem, we propose a real-time semantic segmentation network to achieve high-precision and efficient robotic arm grasp. First, we propose an enhanced feature extraction module for the encoding stage, which can improve feature extraction capabilities without increasing or even reducing the amount of model calculations and parameters. Then, we propose an enhanced feature reconstruction module for the decoding stage, which can better preserve essential features in the feature map restoration stage. To validate the efficacy of our proposed approach, experiments are conducted on the rigid dataset Jacquard and the flexible dataset Electric Wires. Experimental results show that our method can quickly and accurately identify target objects, achieving the best trade-off between accuracy and inference speed. Our method achieves an mIOU of 94.66% and an inference speed of 99.39 FPS on the Jacquard dataset. On the Electric Wires dataset, our method achieves an mIOU of 96.70% with an inference speed of 98.44 FPS.