RER-YOLO: improved method for surface defect detection of aluminum ingot alloy based on YOLOv5.

Aluminum ingot alloy is one of the commonly used materials in industrial production and intelligent manufacturing, whose quality directly affects the performance of aluminum processed products. Therefore, the inspection of surface defects of aluminum ingot alloy is extremely valuable for actual industrial engineering. Aiming at the issues of low detecting precision and the slowly processing rate thatexisted in the traditional target detection methods for aluminum ingot alloy dataset, the YOLOv5-based improvement model RER-YOLO is proposed. Firstly, the aluminum ingot alloy dataset is coped with the image pretreatment methods of rotation, translation, contrast and brightness transformations in a random combination so as to boost the capacity of generalization for model training. Secondly, a multi-scale characteristic extraction network block (Res2Net) is utilized to take the place of the C3 block in the previous YOLOv5 to augment the model's ability that can accurately extract rich features. Finally, an over-parameterization-based re-parameterized convolutional block is utilized in place of the 3×3 convolutional blocks in the Res2Net residual block and baseline model, enlarging the search space of the network and boosting the model's fitting ability while maintaining inference rate. The comparison experimental results demonstrate that the RER-YOLO reaches a mean average precision of 75.1% on the aluminum ingot alloy dataset, which is higher 4.9% than the conventional YOLOv5 and does not increase the inference delay. It also improves the detection accuracy by 12.7% for burr defects, which are fewer in number in the dataset and the defect features are difficult to extract. It can be seen that the presented model in this study has an important reference value towards detecting surface defects in aluminum ingot alloy.

Controlling the Performance of Polymer Lasers via the Cavity Coupling.

The polarization and threshold of distributed feedback (DFB) polymer lasers were controlled by adjusting the cavity coupling. The cavity of DFB polymer lasers consisted of two gratings, which was fabricated by a two-beam multi-exposure holographic technique. The coupling strength of the cavity modes was tuned by changing the angle between the two gratings. The threshold of the polymer lasers decreased with reducing the coupling strength of the cavity modes. A minimum threshold was observed at the lowest coupling strength. Moreover, the azimuthally polarized output of the polymer lasers was modified by changing the cavity coupling. These results may provide additional perspectives to improve the performance of DFB polymer lasers.