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David S. Doermann

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3 papers

1 author row

ICML Conference 2024 Conference Paper

Learning 1-Bit Tiny Object Detector with Discriminative Feature Refinement

Sheng Xu 0007
Mingze Wang
Yanjing Li
Mingbao Lin
Baochang Zhang 0001
David S. Doermann
Xiao Sun

1-bit detectors show impressive performance comparable to their real-valued counterparts when detecting commonly sized objects while exhibiting significant performance degradation on tiny objects. The challenge stems from the fact that high-level features extracted by 1-bit convolutions seem less compelling to reveal the discriminative foreground features. To address these issues, we introduce a Discriminative Feature Refinement method for 1-bit Detectors (DFR-Det), aiming to enhance the discriminative ability of foreground representation for tiny objects in aerial images. This is accomplished by refining the feature representation using an information bottleneck (IB) to achieve a distinctive representation of tiny objects. Specifically, we introduce a new decoder with a foreground mask, aiming to enhance the discriminative ability of high-level features for the target but suppress the background impact. Additionally, our decoder is simple but effective and can be easily mounted on existing detectors without extra burden added to the inference procedure. Extensive experiments on various tiny object detection (TOD) tasks demonstrate DFR-Det’s superiority over state-of-the-art 1-bit detectors. For example, 1-bit FCOS achieved by DFR-Det achieves the 12. 8% AP on AI-TOD dataset, approaching the performance of the real-valued counterpart.

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ICRA Conference 2021 Conference Paper

Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

Leighton Collins
Payam Ghassemi
Ehsan Tarkesh Esfahani
David S. Doermann
Karthik Dantu
Souma Chowdhury

This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e. g. , no-fly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user’s input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.

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ICRA Conference 2021 Conference Paper

Self-Supervised Learning for Monocular Depth Estimation on Minimally Invasive Surgery Scenes

Shuwei Shao
Zhongcai Pei
Weihai Chen
Baochang Zhang 0001
Xingming Wu
Dianmin Sun
David S. Doermann

Self-supervised learning algorithms that compute depth map from monocular videos have achieved remarkable performance on urban scenes and have been applied extensively. These techniques still face significant challenges, however, when applied directly to endoscopic videos because of the brightness variations from frame to frame and inadequate representation learning during the training phase. Inspired by the optical flow for motion alignment between adjacent frames, we design a AFNet with structural stability loss and residual-based smoothness loss to learn the appearance flow across adjacent frames, which handles the brightness inconsistency issue efficaciously. In addition, we propose a novel self-attention mechanism named feature scaling module to alleviate the inadequate representation learning problem. In a comparison study to the current state-of-the-art self-supervised methods explored for urban videos on the SCARED dataset, the developed model surpasses existing methods by a large margin.

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