一种抗粉尘遮挡的卸料孔检测方法

为解决粉镀锌卸料时卸料孔被锌粉遮挡,单一模型目标检测网络或方法无法很好地识别问题,提出一种专门的、融合多种特征模型的检测方法来辅助卸料.首先,用深度可分离卷积代替Tiny-YOLOv3(you only look once version 3)中的传统卷积,并调整损失函数,适应新的训练;接着,用像素统计判断目标区域遮挡情况,运用轨迹特征和形态特征模型对当前帧进行预测;最后,遵循模型融合规则,用预测结果对目标检测网络结果进行优化.实验结果表明在卸料孔长时间遮挡严重情况下,改进后的融合模型表现最好,总体AP(average prediction)达到99.38％,AIOU(average intersection over union)达到88.74％,同时满足实时检测要求,有效解决了粉尘强遮挡条件下工装卸料孔动态检测问题.

Anti-dust-occlusion detection method of handling hole

To solve the problem that the zinc dust cover handling hole when unload the sherardizing reactor automatically, the single object detection model or method can''t detect much well, this paper proposed a special anti-dust-occlusion object detection method with variety of feature model to aid the unloading. Firstly, the depthwise separable convolution was a substitute for the traditional convolution in Tiny-YOLOv3. It also adjusted the loss function to adapt the new training. Secondly, it judged occlusion of target area by pixel statistics and estimated current frame with the trajectory features and shape features. Finally, the prediction results optimized the results of object detection network with fusion rules. The results show that the improved fusion model perform best with the severe and prolonged occlusion on handling hole, the AP and AIOU reach 99.38% and 88.14% respectively. Furthermore, it''s a real-time method, achieving the dynamic detection of the handling hole with serious dust occlusion effectively.