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We address the challenges of achieving high\u2010performance object detection in real\u2010time industrial environments by implementing a structural optimisation approach that dynamically reduces feature maps and prior boxes based on dataset characteristics. The network self\u2010identifies and prunes redundant features by analysing the statistical properties of the images and objects in the datasets, leading to a leaner and more efficient SSD network. This dataset\u2010informed reduction of feature maps and anchor boxes accelerates training and inference speeds and enhances detection precision, which is crucial for accurate part identification in high\u2010throughput production. 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