MACHINE PERCEPTION AND ROBOTICS GROUP

機械知覚&ロボティクスグループ
中部大学

Object detection is a task for acquiring environmental information in automated driving. Object detection is used to detect the position and class of objects in an image. It can be made more accurate by learning with a large amount of supervised data. However, the high cost of annotating the data makes it difficult to create large supervised datasets. Therefore, research using semi-supervised learning for object detection has been attracting attention. Previous studies on semi-supervised learning in object detection tasks have mainly conducted evaluation experiments only on large datasets with many classes, such as MS COCO, and PASCAL VOC. Therefore, the effectiveness of semi-supervised learning for in-vehicle camera data as input has not yet been demonstrated. We examined the effectiveness of semi-supervised learning in object detection when in-vehicle camera data are used as input. We also proposed a class weighted focal loss that employs a unique weighting method that takes into account the class imbalance problem. Experimental results indicate that semi-supervised learning is also effective when vehicle-mounted camera images are used as input. We also confirmed that the proposed mitigates improves the class imbalance problem and improves accuracy.