关键词: 深度学习, 目标检测, 特征提取, 无锚框, 损失函数

Abstract:

In this paper, a one-stage anchor-free channel ship detection algorithm called FoveaSDet is proposed to address the problem of the small size ship, large scale transformation, and complex background in the image of drone aerial. FoveaSDet uses a backbone called SEResNeXt-I, which improves from ResNeXt, to increase the detection accuracy of small objects. Next, to solve the large scale transformation problem, the proposed method uses Foveahead to achieve anchor-free box object detection. The complete IOU loss has used to accomplish the bounding box regression so that the positioning accuracy of the detection box under a complex background can be raised. The experiment results show that the average precision and small objects’ average precision of the FoveaSDet is 71.6% and 47.0% testing on the real aerial image dataset. It has increased by 4.9% and 6.2% compared with FoveaBox, reflecting better detection accuracy and small object detection ability.

Key words: deep learning, object detection, feature extraction, anchor-free, loss function