With the rapid development of smart manufacturing, intelligent safety monitoring in industrial workshops has become increasingly important. To address the challenges of complex backgrounds, target scale variation, and excessive model parameters in worker violation detection, this study proposes ADCP-YOLO, an enhanced lightweight model based on YOLOv8. Here, “ADCP” represents four key improvements: Alterable Kernel Convolution (AKConv), Dilated-Wise Residual (DWR) module, Channel Reconstruction Global Attention Mechanism (CRGAM), and Powerful-IoU loss. These components collaboratively enhance feature extraction, multi-scale perception, and localization accuracy while effectively reducing model complexity and computational cost. Experimental results show that ADCP-YOLO achieves a mAP of 90.6%, surpassing YOLOv8 by 3.0% with a 6.6% reduction in parameters. These findings demonstrate that ADCP-YOLO successfully balances accuracy and efficiency, offering a practical solution for intelligent safety monitoring in smart factory workshops.

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life. The development of the Internet of Things (IoT) relies on the support of base stations, which provide a solid foundation for achieving a more intelligent way of living. In a specific area, achieving higher signal coverage with fewer base stations has become an urgent problem. Therefore, this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization (EPSO) algorithm based on collective prediction, referred to herein as ECPPSO. Introducing a new strategy called neighbor-based evolution prediction (NEP) addresses the issue of premature convergence often encountered by PSO. ECPPSO also employs a strengthening evolution (SE) strategy to enhance the algorithm’s global search capability and efficiency, ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems. To better adapt to the actual communication needs of base stations, this article conducts simulation experiments by changing the number of base stations. The experimental results demonstrate that under the condition of 50 or more base stations, ECPPSO consistently achieves the best coverage rate exceeding 95%, peaking at 99.4400% when the number of base stations reaches 80. These results validate the optimization capability of the ECPPSO algorithm, proving its feasibility and effectiveness. Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.