  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.
