{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T07:28:40Z","timestamp":1772177320028,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"crossref","award":["AB24010110"],"award-info":[{"award-number":["AB24010110"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Pointer-type instruments continue to play an important role in monitoring key operating parameters in industrial environments such as power systems and chemical plants. However, current vision-based reading techniques frequently struggle in terms of accuracy and real-time performance, especially when deployed on edge devices with low processing resources. This study presents an enhanced YOLOv8-based framework that surpasses existing algorithms in terms of accuracy and real-time performance, while maintaining a lightweight design and suitability for deployment on edge devices. A new Context-Aware Spatial-Channel Attention (CASCA) mechanism is suggested, which efficiently captures spatial context and channel dependencies while improving the network\u2019s capacity to discern instrument-specific features. To improve computational efficiency, the model uses structural pruning and replaces standard convolutions with Ghost convolutions, resulting in a smaller model size and lower inference complexity while maintaining accuracy. Furthermore, a hybrid approach combining U2Net-based fine segmentation and Hough transform-based line detection is used to accurately recover the pointer\u2019s orientation and scale. Comprehensive experiments show that the suggested technique achieves a detection accuracy of 96.1% while boosting inference speed, validating its appropriateness for real-time, high-precision sensor data in industrial settings.<\/jats:p>","DOI":"10.3390\/a19030171","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T10:59:16Z","timestamp":1772017156000},"page":"171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CASCA-YOLOv8: An Enhanced Framework for Accurate Pointer-Type Instrument Reading Recognition"],"prefix":"10.3390","volume":"19","author":[{"given":"Mingyu","family":"Zhao","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanlin","family":"Sun","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bensheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Tan","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, J., Zeng, C., Yao, Y., Zhang, S., and Yang, S. 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