This research proposes LightIndust-Net, a lightweight object detection and instance segmentation algorithm specifically designed for robotic arm operations in industrial assembly scenarios. The algorithm effectively addresses the challenge of balancing accuracy and computational efficiency in current robotic operation systems through an innovative StarNet backbone network, Cross-Stage Star Bottleneck (CSSB) modules, and a Shared Convolutional Separator Segmentation Head (Seg_SCSS). Experimental evaluation shows that LightIndust-Net achieves a 22.1% improvement in computational efficiency and a 37.7% reduction in model parameters relative to the baseline approach, while maintaining or even improving detection and instance segmentation accuracy, particularly excelling in processing complex industrial component edge details and shape features. This efficient and precise visual perception solution provides reliable support for real-time robotic arm assembly tasks in resource-constrained environments.