This paper details the challenges in applying two computer vision systems, an
EfficientDET supervised learning model and the unsupervised RX spectral
classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and
rescue (WSAR) effort in Japan and identifies 3 directions for future research.
There have been at least 19 proposed approaches and 3 datasets aimed at
locating missing persons in drone imagery, but only 3 approaches (2
unsupervised and 1 of an unknown structure) are referenced in the literature as
having been used in an actual WSAR operation. Of these proposed approaches, the
EfficientDET architecture and the unsupervised spectral RX classifier were
selected as the most appropriate for this setting. The EfficientDET model was
applied to the HERIDAL dataset and despite achieving performance that is
statistically equivalent to the state-of-the-art, the model fails to translate
to the real world in terms of false positives (e.g., identifying tree limbs and
rocks as people), and false negatives (e.g., failing to identify members of the
search team). The poor results in practice for algorithms that showed good
results on datasets suggest 3 areas of future research: more realistic datasets
for wilderness SAR, computer vision models that are capable of seamlessly
handling the variety of imagery that can be collected during actual WSAR
operations, and better alignment on performance measures.

应用 EfficientDET 和无监督 RX 光谱分类器两个计算机视觉系统在 Wu-Murad 野外搜救努力中，对 98.9GB 的无人机图像的挑战进行详细说明，并确定了未来研究的 3 个方向。

野外合成孔径雷达计算机视觉的未解之谜及对 Patricia Wu-Murad 的搜索

Open Problems in Computer Vision for Wilderness SAR and The Search for  Patricia Wu-Murad

Marine animals and deep underwater objects are difficult to recognize and
monitor for safety of aquatic life. There is an increasing challenge when the
water is saline with granular particles and impurities. In such natural
adversarial environment, traditional approaches like CNN start to fail and are
expensive to compute. This project involves implementing and evaluating various
object detection models, including EfficientDet, YOLOv5, YOLOv8, and
Detectron2, on an existing annotated underwater dataset, called the
Brackish-Dataset. The dataset comprises annotated image sequences of fish,
crabs, starfish, and other aquatic animals captured in Limfjorden water with
limited visibility. The aim of this research project is to study the efficiency
of newer models on the same dataset and contrast them with the previous results
based on accuracy and inference time. Firstly, I compare the results of YOLOv3
(31.10% mean Average Precision (mAP)), YOLOv4 (83.72% mAP), YOLOv5 (97.6%),
YOLOv8 (98.20%), EfficientDet (98.56% mAP) and Detectron2 (95.20% mAP) on the
same dataset. Secondly, I provide a modified BiSkFPN mechanism (BiFPN neck with
skip connections) to perform complex feature fusion in adversarial noise which
makes modified EfficientDet robust to perturbations. Third, analyzed the effect
on accuracy of EfficientDet (98.63% mAP) and YOLOv5 by adversarial learning
(98.04% mAP). Last, I provide class activation map based explanations (CAM) for
the two models to promote Explainability in black box models. Overall, the
results indicate that modified EfficientDet achieved higher accuracy with
five-fold cross validation than the other models with 88.54% IoU of feature
maps.

该研究使用现有的水下数据集 Brackish-Dataset，评估了多种目标检测模型（包括 EfficientDet，YOLOv5 等）在含盐度高、可见度低的自然复杂环境下的效果，其中，通过修改 EfficientDet 的机制，实现了对抗性干扰下高 Iou 的复杂特征融合，并提供了针对模型黑盒性的类激活映射解释。研究结果表明，修改后的 EfficientDet 在五倍交叉验证下，比其他模型表现更好，具有更高的 IoU。

DeepSeaNet: 利用 EfficientDet 提升水下物体检测

DeepSeaNet: Improving Underwater Object Detection using EfficientDet

Model efficiency has become increasingly important in computer vision. In
this paper, we systematically study neural network architecture design choices
for object detection and propose several key optimizations to improve
efficiency. First, we propose a weighted bi-directional feature pyramid network
(BiFPN), which allows easy and fast multiscale feature fusion; Second, we
propose a compound scaling method that uniformly scales the resolution, depth,
and width for all backbone, feature network, and box/class prediction networks
at the same time. Based on these optimizations and better backbones, we have
developed a new family of object detectors, called EfficientDet, which
consistently achieve much better efficiency than prior art across a wide
spectrum of resource constraints. In particular, with single model and
single-scale, our EfficientDet-D7 achieves state-of-the-art 55.1 AP on COCO
test-dev with 77M parameters and 410B FLOPs, being 4x - 9x smaller and using
13x - 42x fewer FLOPs than previous detectors. Code is available at
this https URL

本文系统地研究了神经网络结构设计选择以提高计算机视觉中目标检测的模型效率，提出多个优化方案，包括带权重的双向特征金字塔网络、统一调整骨干网络、特征网络、盒类预测网络的分辨率、深度和宽度的复合调节方法，成功开发了名为 EfficientDet 的新一代目标检测器，拥有更高的性能和更小的计算量。