| Summary: | In response to the issues of relatively low image contrast, blurry boundaries of infrared small targets, and various environmental noise interferences in thermal imaging systems, resulting in reduced target recognition rates and tracking effectiveness. This paper proposes an infrared target image recognition algorithm based on fuzzy comprehensive assessment. Built upon infrared image sequences captured by an on-board thermal imager, the algorithm employs grayscale processing, neighborhood mean filtering, and second-order difference method to filter and enhance the infrared target images. This process improves the quality of infrared target images and enhances the contrast between targets and backgrounds. The algorithm utilizes the maximum interclass variance method for segmenting the infrared target images, combining it with the Canny operator to extract target edges, thereby obtaining clearer details of the infrared target edges. Finally, based on the extracted target feature information, the algorithm calculates the confidence of each marked region, establishes the membership function of target likelihood, employs a comprehensive weighted approach to construct the target confidence function, and compares the confidence of marked regions with that of the template image to achieve precise target recognition. Experimental validation against theoretical methods and comparisons with other approaches demonstrate the effectiveness and feasibility of the proposed method, providing theoretical support for reliable target tracking in electro-optical imaging systems.
|
|---|