目标检测与跟踪是机载光电设备至关重要的功能模块，其检测跟踪的性能直接关系到目标感知的精度。近年来基于Siamese 网络的改进跟踪算法在各种挑战性的数据集上取得了优异的效果，但大多数改进算法采用局部搜索策略，无法更新模板，且模板会引入背景干扰，最终因跟踪点漂移导致跟踪失败。为了解决这些问题，本文提出了一种结合目标边缘检测的改进全连接Siamese 跟踪算法，该算法利用目标的轮廓模板代替边界框模板，减少了背景杂波的干扰；同时，在Siamese 网络的基础上增加了一路改进tiny-YOLOv3 目标检测网络，利用K 均值聚类找到最合适的锚框（anchor box），引入了扩张模块层来扩展感受野，增加了系统的抗遮挡能力，提高机载光电设备的目标捕获概率。在基准测试数据集以及挂飞数据集基础上的仿真测试性能表明本文提出的改进模型特别适合机载光电设备在跟踪与重捕复杂环境下的运动目标，在长期跟踪中能够更好地适应目标的变形和遮挡，提升系统响应时间与适应性。

Object detection and tracking is an essential module in airborne optoelectronic equipment, and its performance is directly related to the accuracy of object perception. Improved Siamese network tracking algorithms have produced excellent results for various challenging datasets recently, but most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will result in tracking drift and eventually cause tracking failure. To solve these problems, this paper proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection; the algorithm uses the contour template of the target instead of the bounding box template to reduce the background clutter interference. A branch is added to the Siamese network to improve the tiny-YOLOv3 object detection network, where K-means clustering is used to find the most suitable anchor box. An expansion module layer is introduced to expand the receptive field. Therefore, our proposed model increases the anti-occlusion ability of the system and improves the object recapture probability of airborne optoelectronic equipment. The results of a simulation of benchmark test data set and a flight dataset show that the improved model is especially suitable for tracking and recapture of moving objects in complex environments; in addition, it can better adapt to deformed or occluded objects in long-term tracking, which improves the system response time and adaptability.（LSJGMS1811）。