To address the issues of low trajectory planning efficiency, high motion impact, and poor operational stability in robotic arms during the automatic loading and unloading of aluminum blocks in coating machinery, a time-optimal trajectory optimization method based on a dynamically adaptive Particle Swarm Optimization (PSO) algorithm is proposed. First, the loading and unloading process of aluminum block components is described, followed by a kinematic analysis of the robotic arm in joint space. Then, the “3-5-3” hybrid polynomial interpolation method is used to fit the robotic arm’s motion trajectory and simulate the analysis. Finally, with the robotic arm’s operation time as the objective function, the dynamically adaptive PSO algorithm is applied to optimize the trajectory constructed by hybrid polynomial interpolation, achieving time-optimal trajectory planning for aluminum block handling. The results demonstrate that the proposed method successfully reduces the trajectory planning times for condition 1 and condition 2 from 6 s to 3.59 s and 3.14 s, respectively, improving overall efficiency by 40.2% and 47.7%. This confirms the feasibility of the method and significantly enhances the efficiency of automated loading and unloading tasks for aluminum blocks in coating machinery. The proposed method is highly adaptable and well-suited for real-time trajectory optimization of robotic arms. It can also be broadly applied to other robotic systems and manufacturing processes, enhancing operational efficiency and stability.

To address the challenges of poor image quality and low detection accuracy in low-light environments during coating workshop inspections, this paper proposes a low-light image enhancement method based on zero-reference depth curve estimation, termed Zero-PTDCE. A low-light image dataset, PT-LLIE, tailored for coating workshop scenarios is constructed, encompassing various industrial inspection conditions under different lighting environments to enhance model adaptability. Furthermore, an enhancement network integrating a lightweight denoising module and depthwise separable dilated convolution is designed to reduce noise interference, expand the receptive field, and improve image detail restoration. The network training process employs a multi-constraint strategy by incorporating perceptual loss (L_p), color loss (L_c), spatial consistency loss (L_s), exposure loss (L_e), and total variation smoothness loss (L_tv) to ensure balanced brightness, natural color reproduction, and structural integrity in the enhanced images. Experimental results demonstrate that, compared to existing low-light image enhancement methods, the proposed approach achieves superior performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE), while maintaining high computational efficiency. Beyond general visual enhancement, Zero-PTDCE significantly improves the visibility of fine surface features and defect patterns under low-light conditions, which is crucial for the accurate assessment of coating quality, including defect identification such as uneven thickness, delamination, and surface abrasion. This work provides a reliable image enhancement solution for intelligent inspection systems and supports both the automated operation and material quality evaluation in modern coating workshops, contributing to the broader goals of intelligent manufacturing and material characterization.

To address the challenges of small fastener targets, complex backgrounds, and the low efficiency of traditional manual inspection in coating workshop scenarios, this paper proposes an improved Cascade R-CNN-based fastener detection method. A VOC-format dataset was constructed covering three target categories—Marking-painted fastener, Fastener, and Fallen off—which represents typical inspection scenarios of coating equipment under diverse operating conditions and enhances the adaptability of the model. Within the Cascade R-CNN framework, three improvements were introduced: the Convolutional Block Attention Module (CBAM) was integrated into the ResNet-101 backbone to enhance feature representation of small objects; anchor scales were reduced to better align with the actual size distribution of fasteners; and Soft-NMS was adopted in place of conventional NMS to effectively reduce missed detections in overlapping regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 96.60% on the self-constructed dataset, with both Precision and Recall exceeding 95%, significantly outperforming Faster R-CNN and the original Cascade R-CNN. The method enables accurate detection and missing-state recognition of fasteners in complex backgrounds and small-object scenarios, providing reliable technical support for the automation and intelligence of printing equipment inspection.