Object detection in aerial imagery presents a significant challenge due to
large scale variations among objects. This paper proposes an evolutionary
reinforcement learning agent, integrated within a coarse-to-fine object
detection framework, to optimize the scale for more effective detection of
objects in such images. Specifically, a set of patches potentially containing
objects are first generated. A set of rewards measuring the localization
accuracy, the accuracy of predicted labels, and the scale consistency among
nearby patches are designed in the agent to guide the scale optimization. The
proposed scale-consistency reward ensures similar scales for neighboring
objects of the same category. Furthermore, a spatial-semantic attention
mechanism is designed to exploit the spatial semantic relations between
patches. The agent employs the proximal policy optimization strategy in
conjunction with the evolutionary strategy, effectively utilizing both the
current patch status and historical experience embedded in the agent. The
proposed model is compared with state-of-the-art methods on two benchmark
datasets for object detection on drone imagery. It significantly outperforms
all the compared methods.

提出了一种演化强化学习代理，结合了粗到精的目标检测框架，用于优化尺度以更有效地检测航拍图像中的目标。通过一组针对定位准确性、预测标签准确性和临近补丁尺度一致性的奖励，引导尺度优化；并设计了一种空间语义关注机制，利用补丁之间的空间语义关系。该模型在无人机图像目标检测这两个基准数据集上与最先进的方法进行比较，显著优于所有对比方法。

利用进化强化学习进行无人机图像目标检测的规模优化

Scale Optimization Using Evolutionary Reinforcement Learning for Object  Detection on Drone Imagery

The 8 bits quantization has been widely applied to accelerate network
inference in various deep learning applications. There are two kinds of
quantization methods, training-based quantization and post-training
quantization. Training-based approach suffers from a cumbersome training
process, while post-training quantization may lead to unacceptable accuracy
drop. In this paper, we present an efficient and simple post-training method
via scale optimization, named EasyQuant (EQ),that could obtain comparable
accuracy with the training-based method.Specifically, we first alternately
optimize scales of weights and activations for all layers target at
convolutional outputs to further obtain the high quantization precision. Then,
we lower down bit width to INT7 both for weights and activations, and adopt
INT16 intermediate storage and integer Winograd convolution implementation to
accelerate inference.Experimental results on various computer vision tasks show
that EQ outperforms the TensorRT method and can achieve near INT8 accuracy in 7
bits width post-training.

本文提出一种名为 EasyQuant (EQ) 的简单有效的后训练量化方法，通过尺度优化来获得类似于基于训练方法的精度。具体地，我们针对卷积输出的所有层交替优化权重和激活的规模，以进一步获取高量化精度。然后，我们将位宽降低到 INT7，同时采用 INT16 中间存储和整数 Winograd 卷积实现加速推断。各种计算机视觉任务的实验结果表明，EQ 优于 TensorRT 方法，并且在经过 7 位宽后训练可达到接近 INT8 的精度。