  Deep learning has been constantly improving in recent years and a significant
number of researchers have devoted themselves to the research of defect
detection algorithms. Detection and recognition of small and complex targets is
still a problem that needs to be solved. The authors of this research would
like to present an improved defect detection model for detecting small and
complex defect targets in steel surfaces. During steel strip production
mechanical forces and environmental factors cause surface defects of the steel
strip. Therefore the detection of such defects is key to the production of
high-quality products. Moreover surface defects of the steel strip cause great
economic losses to the high-tech industry. So far few studies have explored
methods of identifying the defects and most of the currently available
algorithms are not sufficiently effective. Therefore this study presents an
improved real-time metallic surface defect detection model based on You Only
Look Once (YOLOv5) specially designed for small networks. For the smaller
features of the target the conventional part is replaced with a depth-wise
convolution and channel shuffle mechanism. Then assigning weights to Feature
Pyramid Networks (FPN) output features and fusing them increases feature
propagation and the networks characterization ability. The experimental results
reveal that the improved proposed model outperforms other comparable models in
terms of accuracy and detection time. The precision of the proposed model
achieved by @mAP is 77.5% on the Northeastern University Dataset NEU-DET and
70.18% on the GC10-DET datasets
