Detecting industrial defects presents formidable challenges, as anomalies often appear as tiny, low-contrast objects on complex surfaces with varied orientations. This work introduces two lightweight attention modules that strengthen feature discrimination while incurring minimal computational overhead. Our lightweight anisotropic spatial-channel attention (LASCA) module captures directional texture cues using bidirectional depthwise-separable convolutions and fuses spatial-channel information via a soft-gated mechanism. We further propose the Adaptive Energy-Guided Interaction Stream (AEGIS Stream), a novel feature enhancement block designed for high-precision small-object detection. Unlike conventional attention mechanisms, the AEGIS Stream employs a parameter-free energy-guided slicing strategy to capture fine-grained local saliency, coupled with a global interaction stream that aggregates cross-spatial context. Integrating this module into the C2f structure forms the AEGIS-C2f module, which enables robust feature fusion in complex industrial backgrounds. On the GC10-DET benchmark, our model reaches 68.9% mAP ₅₀ , a +1.7 percentage-point gain over YOLOv8n, with only 3.01 M parameters and 8.1 GFLOPs. Substantial improvements are also recorded on NEU-DET (+5.3 pp mAP ₅₀ ), DeepPCB (+0.3 pp), and VOC2007 (+0.6 pp). Ablation studies confirm that LASCA and AEGIS deliver complementary benefits. The resulting framework supports real-time, high-precision defect inspection with negligible added cost.