Efficient and accurate detection of small objects in manufacturing settings, such as defects and cracks, is crucial for ensuring product quality and safety. To address this issue, we proposed a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to efficiently handle multi-scale features intrinsic to manufacturing environments. Additionally, Deformable Net is used that contorts and conforms to the geometric variations of defects, bringing precision in detecting even the minuscule and complex features. Then, we incorporated an attention mechanism called Convolutional Block Attention Module in each block of our base ResNet50 network to selectively emphasize informative features and suppress less useful ones. After that we incorporated RoI Align, replacing RoI Pooling for finer region-of-interest alignment and finally the integration of Focal Loss effectively handles class imbalance, crucial for rare defect occurrences. The rigorous evaluation of our model on both the NEU-DET and Pascal VOC datasets underscores its robust performance and generalization capabilities. On the NEU-DET dataset, our model exhibited a profound understanding of steel defects, achieving state-of-the-art accuracy in identifying various defects. Simultaneously, when evaluated on the Pascal VOC dataset, our model showcases its ability to detect objects across a wide spectrum of categories within complex and small scenes.

在制造环境中，高效准确地检测小型物体，如缺陷和裂纹，对于确保产品质量和安全至关重要。我们提出了一种综合策略，通过将Faster R-CNN与先进的方法结合起来，有效解决了这个问题。我们的模型在NEU-DET和Pascal VOC数据集上进行了严格评估，验证了其鲁棒的性能和通用性。在NEU-DET数据集上，我们的模型表现出对钢铁缺陷的深入理解，在识别各种缺陷方面实现了最先进的准确性。同时，在Pascal VOC数据集上评估时，我们的模型展示了在复杂且小型场景中检测各类对象的能力。

DANet：通过高效的变形注意力网络提升小目标检测