Factors, such as rapid relative motion, clutter background, etc., make robust
small aerial target detection for airborne infrared detection systems a
challenge. Existing methods are facing difficulties when dealing with such
cases. We consider that a continuous and smooth trajectory is critical in
boosting small infrared aerial target detection performance. A simple and
effective small aerial target detection method for airborne infrared detection
system using light gradient boosting model (LightGBM) and trajectory
constraints is proposed in this article. First, we simply formulate target
candidate detection as a binary classification problem. Target candidates in
every individual frame are detected via interesting pixel detection and a
trained LightGBM model. Then, the local smoothness and global continuous
characteristic of the target trajectory are modeled as short-strict and
long-loose constraints. The trajectory constraints are used efficiently for
detecting the true small infrared aerial targets from numerous target
candidates. Experiments on public datasets demonstrate that the proposed method
performs better than other existing methods. Furthermore, a public dataset for
small aerial target detection in airborne infrared detection systems is
constructed. To the best of our knowledge, this dataset has the largest data
scale and richest scene types within this field.

提出了一种简单而有效的使用 LightGBM 和轨迹约束的空中红外检测系统中小型空中目标检测方法，并通过对公共数据集进行实验证明了该方法的优势，同时还构建了一个该领域内数据量最大、场景类型最丰富的公共数据集。

利用 LightGBM 和轨迹约束的机载红外探测系统的小型空中目标检测

Small Aerial Target Detection for Airborne Infrared Detection Systems  using LightGBM and Trajectory Constraints

We study the characterization and computation of general policies for
families of problems that share a structure characterized by a common reduction
into a single abstract problem. Policies $\mu$ that solve the abstract problem
P have been shown to solve all problems Q that reduce to P provided that $\mu$
terminates in Q. In this work, we shed light on why this termination condition
is needed and how it can be removed. The key observation is that the abstract
problem P captures the common structure among the concrete problems Q that is
local (Markovian) but misses common structure that is global. We show how such
global structure can be captured by means of trajectory constraints that in
many cases can be expressed as LTL formulas, thus reducing generalized planning
to LTL synthesis. Moreover, for a broad class of problems that involve integer
variables that can be increased or decreased, trajectory constraints can be
compiled away, reducing generalized planning to fully observable
non-deterministic planning.

本文研究了具有共同特征的问题集合的一般策略的特征和计算，并介绍了如何通过利用轨迹约束以及 LTL 公式来消除策略的终止条件以及降低规划的复杂度。