To improve the efficiency of the extravehicular activity ( EVA ) , an approach for object detection inspired by visual perceptual theory was proposed. First, certain area of the image was col-lected from visual observation, then the feature maps of binarized normed gradient were extracted, and the bounding box of the object was predicted. Then an area of the image bigger than the boun-ding box was chosen to feed into the deep convolutional neural networks CNN as the input for class recognition and precise object location. Experiments were conducted to verify the proposed method with the dataset established by ourselves. The results showed that the mAP of this approach was 88. 2％ and the average compute time was 0. 047 s, which could satisfy the demands of EVA. This approach may serve as a reference for the intellectualization and informatization of EVA tasks. It is of great significance for the improvement of EVA efficiency.%为提高航天员出舱活动(EVA)的工效,提出了一种基于视觉感知启发的物体识别方法.首先对视觉观察到的一定区域内的图像进行采集,然后进行二值化赋范梯度的特征提取,并预测物体所在区域的矩形框,选取比该矩形框扩大一定范围的图像作为输入,传递给深度卷积神经网络CNN进行类别识别和精定位.在自建的数据集上进行测试验证,结果表明:该方法达到了88.2％的平均识别准确率,识别速率为0.047 s,可以满足舱外物体识别需求.该方法可为信息化、智能化的出舱活动任务提供参考,对提高出舱活动任务的工效具有重要意义.
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