At present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance. Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural network of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCAL VOC 2007 and 72.3% mAP in PASCAL VOC 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection.

Angle calibrations are widely used in various fields of science and technology, while in the high-precision angle calibrations, a complete closure method which is complex and time-consuming is common. Therefore, in order to improve the measurement efficiency and maintain the accuracy of the complete closure method, an improved calibration method was proposed and verified by the calibration of a high-precision angle comparator with sub-arc-second level. Firstly, a basic principle and algorithm of angle calibration based on complete closure and symmetry connection theory was studied. Then, depending on the pre-established calibration system, the comparator was respectively calibrated by two calibration methods. Finally, by comparing En values of two calibration results, the effectiveness of the improved method was verified. The calibration results show that the angle comparator has a stable angle position error of 0:1700 and a measurement uncertainty of 0:0500 (k = 2). Through method comparisons, it was shown that the improved calibration method can greatly reduce calibration time and improve the calibration efficiency while ensuring the calibration accuracy, and with the decrease of measurement interval, the improvement of calibration efficiency was more obvious.