Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor

Abstract

Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.

Keywords: density-distance space; infrared image sensor; small target detection; trajectory correction fuze.

Figure 1

Model of the trajectory correction fuze and the infrared image sensor.

Figure 2

Flowchart of the density-distance space method.

Figure 3

Calculation process of density and distance of pixels.

Figure 4

Detection results of the two targets with different size in the same condition. Both (a) and (b) contain 4 images, which are the original image, detection result, target region mesh and ρ-σ space, respectively.

Figure 5

Process of the APG method. From left to right is the two-step growth process of the No. 1 seed.

Figure 6

Growth features of seeds: (a) contains 5 seeds in different regions, (b) is the seed of the target region and (c–f) are the seeds in clutter regions.

Figure 7

Experiment of detection capability of the infrared imager. The target observation result is shown on the left. The equipment is shown on the right.

Figure 8

Simulation of the anti-noise ability of the algorithm. The SNR decreases from left to right.

Figure 9

Simulation of the detection ability of multi-target for six images. The first row is the original images, the second row is the corresponding density-distance space and the bottom row is the detection results.

Figure 10

Simulation of the ROC curves of different algorithms and corresponding AUC values: (a–e) are the comparison results of 5 different sequences, where each sequence has 200 frames, and (f) is any frame of image in sequence 2, which is mainly used to specifically explain the reason for the low AUC value of our method.

Figure 11

Experiment of HIL.