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First, a domain-specific\n                    <jats:italic>Augmentation Algorithm<\/jats:italic>\n                    is introduced to expand the training dataset, mitigate overfitting, and enhance the model\u2019s generalization by emphasizing critical visual features associated with structural defects. Second, a guided\n                    <jats:italic>CNN Development Mechanism<\/jats:italic>\n                    facilitates architectural optimization by systematically refining filter sizes, neuron counts, and convolutional block configurations, enabling high performance with reduced parameter overhead. Third, the proposed\n                    <jats:italic>PalletDetect Module (PD-M)<\/jats:italic>\n                    enhances computational efficiency by adaptively refining feature representations at the input tensor level, reducing complexity while preserving discriminative capacity. These algorithms collectively produce\n                    <jats:bold>PDNet<\/jats:bold>\n                    , a compact CNN that enables real-time pallet racking inspection on resource-constrained edge devices. PDNet achieves an accuracy of 92.07%, with a computational complexity of only 32.31 million multiply\u2013accumulate operations (MMAC) and a compact memory footprint of 31.36\u00a0MB. Compared to modern lightweight CNNs such as MobileNetV3 and ShuffleNetV2, PDNet offers a superior balance between accuracy, speed, and computational efficiency, demonstrating its potential for real-time industrial inspection applications.\n                  <\/jats:p>","DOI":"10.1007\/s44163-025-00542-z","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T11:20:43Z","timestamp":1762255243000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PDNet: a lightweight attention-guided CNN for efficient pallet racking defect detection on edge devices"],"prefix":"10.1007","volume":"5","author":[{"given":"Rahima","family":"Khanam","sequence":"first","affiliation":[]},{"given":"Muhammad","family":"Hussain","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Hill","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"unstructured":"Cisco-Eagle: Pallet Rack Inspection. https:\/\/www.cisco-eagle.com\/category\/6739\/pallet-rack-inspection 2024","key":"542_CR1"},{"unstructured":"Damotech: Warehouse Rack Maintenance to Maximize Lifespan and Savings. https:\/\/www.damotech.com\/blog\/warehouse-rack-maintenance-to-maximize-lifespan-and-savings Accessed Accessed: 23 April, 2024","key":"542_CR2"},{"unstructured":"Landgraf, J., Kompenhans, M., Christ, T., Roland, T., Heinig, A.: Computer vision for industrial defect detection. 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