To better balance binary feature descriptor algorithms based on manual learning

which have superior real-time performance

and binary feature descriptor algorithms based on optimization study

which have robust performance

this paper presents a binary feature description algorithm for fast optimization screening of a multi-scale rectangular area (referred to as MRFO). The typical workpiece target in satellite assembly is identified. The proposed description algorithm divides images according to the pixel gray value and gradient direction and simultaneously smoothens each sub-image with different Gaussian kernel functions to establish a multi-scale image set. Candidate rectangular areas are rapidly extracted through the constraint condition from the sub-image of multi-scale or the multi-scale feature point neighborhood. In the training phase

the proposed algorithm calculates the score and optimal threshold of candidate rectangular areas by using optimization study and selects the subset that has strong distinction and low correlation. In the testing phase

the proposed description algorithm calculates the response value of selected rectangular areas in the multi-scale feature point neighborhood and employs the optimal threshold for binarization to constitute the binary description vector of feature points. The experiment proved that the binary feature description algorithm for fast optimization screening of a multi-scale rectangular area demonstrates a robust performance based on the ROC curve and the recall rate statistical result under the condition of 80% accuracy rate. The average accuracy is higher by 8% to 12% than that of compared algorithms. In real video images

the proposed description algorithm can identify the typical workpiece target accurately. The experiment also proved the superior real-time performance of the proposed algorithm; the execution time in the training phase is only 4.35% of the traditional optimization learning algorithm (only slightly higher than that of the binary description algorithm based on manual learning in the testing phase). Compared with the traditional scheme of binary feature description algorithms and float feature description algorithms

the proposed feature description algorithm can overcome interference from all types of perspective

scale

rotation transformation

and influence of similar background information. The proposed algorithm can identify the typical workpiece target accurately. The algorithm can help improve the accuracy and efficiency of satellite assembly and enhance the automation level of domestic related industries. The universality of the method is strong

and it has a good application potential.