Assessing the applicability of common performance metrics for real-world infrared small-target detection

Infrared small target detection (IRSTD) is a challenging task in computer vision. During the last two decades, researchers' efforts are devoted to improving detection ability of IRSTDs. Despite the huge improvement in designing new algorithms, lack of extensive investigation of the evaluation metrics are evident. Therefore, in this paper, a systematic approach is utilized to: First, investigate the evaluation ability of current metrics; Second, propose new evaluation metrics to address shortcoming of common metrics. To this end, after carefully reviewing the problem, the required conditions to have a successful detection are analyzed. Then, the shortcomings of current evaluation metrics which include pre-thresholding as well as post-thresholding metrics are determined. Based on the requirements of real-world systems, new metrics are proposed. Finally, the proposed metrics are used to compare and evaluate four well-known small infrared target detection algorithms. The results show that new metrics are consistent with qualitative results.

Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps

This paper proposes a fast and accurate surface normal estimation method which can be directly used on depth maps (organized point clouds). The surface normal estimation process is formulated as a closed-form expression. In order to reduce the effect of measurement noise, the averaging operation is utilized in multi-direction manner. The multi-direction normal estimation process is reformulated in the next step to be implemented efficiently. Finally, a simple yet effective method is proposed to remove erroneous normal estimation at depth discontinuities. The proposed method is compared to well-known surface normal estimation algorithms. The results show that the proposed algorithm not only outperforms the baseline algorithms in term of accuracy, but also is fast enough to be used in real-time applications.

Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.