Mr. Jianlin Zhang Profile

Jianlin Zhang

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Proceedings Article | 19 February 2024 Paper

Detection of dim small targets in strong backgrounds based on local feature maximum difference

Tao Ma, Jinhao Xu, Xiaoping Qi, Jianlin Zhang

Proceedings Volume 13063, 130632H (2024) https://doi.org/10.1117/12.3021453

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The essence of weak and small target detection is to achieve separation between the target and the background. However, for extremely low contrast (<0.1) and extremely low signal-to-noise ratio (<=3dB, most of which are less than 0dB) targets in strong backgrounds, the intensity difference between the target and the background is very small. However, in strong backgrounds, the fluctuation of the target submerges it and makes it difficult to directly separate it. Therefore, in order to achieve target detection in such extreme scenarios, it is necessary to construct feature descriptions that can effectively separate them. Based on this idea, this article summarizes the target detection task as a local feature difference maximization model suitable for all spatial target detection, and uses the local maximum Pearson correlation coefficient as the feature extraction equation to calculate the correlation between the two patches. Based on the small correlation between the target and the local background, and the high correlation between the background and the background, the separation of the target and the background is completed. Then using a constant local signal-to-noise ratio feature extraction equation to enhance the Pearson correlation results. A large number of experimental results show that the model and algorithm proposed in this paper can effectively detect targets with extremely low signal-to-noise ratios in strong backgrounds.

Proceedings Article | 19 February 2024 Paper

Enhanced correlation filter tracking: anti-occlusion and re-detection

Wan Li, Yuxing Wei, Jianlin Zhang

Proceedings Volume 13063, 130631D (2024) https://doi.org/10.1117/12.3021374

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Target tracking in computer vision is a challenging task that requires both accuracy and robustness, especially in the presence of occlusions and complex backgrounds. To tackle these challenges, this paper proposes a novel tracking approach based on correlation filters, which incorporates reliability assessment and re-detection modules. The proposed approach leverages enhanced color histogram scores in combination with correlation filter response maps to improve the accuracy of target localization. By considering metrics such as maximum response value, average peak-to-correlation energy, and peak-to-sidelobe ratio, the reliability of the tracking process is evaluated, providing insights into its performance and stability. In cases of tracking failure, a re-detection module is activated to reacquire the lost target and resume the tracking task. This module dynamically adjusts and updates the model to enhance the accuracy and robustness of the tracking process. Extensive experiments are conducted on popular benchmark datasets includingOTB50, OTB100, as well as custom datasets. The results demonstrate the superiority of the proposed approach, particularly in challenging scenarios with occlusions and complex scenes, outperforming baseline trackers and other state-of-the-art tracking methods.

Proceedings Article | 19 February 2024 Paper

FF-DETR: feature-fusion transformers for end-to-end object detection

Jinhao Xu, Tao Ma, Junping Xu, Jianlin Zhang

Proceedings Volume 13063, 130631C (2024) https://doi.org/10.1117/12.3021368

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Object detection is a challenging task in computer vision that involves predicting both the class and the location of objects in an image. Most existing methods rely on convolutional neural networks and hand-crafted modules, such as anchor boxes and non-maximum suppression. Recently, a novel end-to-end approach called DETR was proposed, which uses a transformer encoder-decoder structure to model object detection as a set prediction problem. However, DETR suffers from some limitations, such as poor performance on small objects and slow convergence speed. In this paper, we propose FF-DETR, a feature-fusion detection transformer that improves the performance and convergence speed of DETR-like models. FF-DETR introduces three feature fusion modules: (1) Contour Fusion FPN, which fuses multi-scale features using self-attention and deformable convolution; (2) Position-Content Query Fusion, which initializes the content query features by fusing the position query features and the encoder output features; and (3) Global Decoder Layer Fusion, which fuses the outputs of each decoder layer and updates the position query features iteratively. We conduct experiments on the COCO dataset and show that FF-DETR outperforms DETR and other variants in terms of accuracy and efficiency.

Proceedings Article | 8 June 2023 Paper

An anchor-free pedestrian detection algorithm based on visible-thermal feature fusion

Gaofan Zhou, Peng Qin, Chuangming Tang, Qingqing Li, Yuxing Wei, Jianlin Zhang

Proceedings Volume 12707, 1270749 (2023) https://doi.org/10.1117/12.2681377

KEYWORDS: Feature fusion, Object detection

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Visible and thermal modalities are strongly complementary in object signal representation. Using the two modalities simultaneously is beneficial to reduce the impact of illumination variation on pedestrian detection. To effectively utilize multimodal information, this paper proposes an anchor-free multimodal pedestrian algorithm. First, a modal feature fusion module is proposed, which executes modal fusion by decaying dense connections and combines convolution with the self-attention mechanism to account for local and global information between the modalities. Secondly, through the multiwindow global context module and the pyramid feature fusion module, a new feature pyramid network enhanced by global context information is proposed. On the visible-thermal pedestrian detection datasets KAIST, CVC-14 and LLVIP, the proposed method achieves 5.67%, 20.51% and 2.21% average miss rate respectively, which is better than the mainstream algorithms.

Proceedings Article | 8 June 2023 Paper

Stable tracking for low contrast extended target based on edge feature point matching

Mengting Zheng, Xiaoping Qi, Jianlin Zhang, Yuxing Wei

Proceedings Volume 12707, 1270729 (2023) https://doi.org/10.1117/12.2681359

KEYWORDS: Detection and tracking algorithms, Image enhancement, Light sources and illumination, Feature extraction, Image processing

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To solve the tracking drift caused by rotation, uneven illumination, and beyond the field of view in the moving process of the target with low contrast extension, a method of edge feature points matching is proposed in this paper. Specifically, feature points are extracted from image edges to improve the stability of feature points matching. In addition, combined Non-Local algorithm and improved Contrast Limited Adaptive Histogram Equalization algorithms are used to enhance the image. Experimental results show that this method has satisfactory performance, has good anti-rotation characteristics, can be stably tracked when the target reappears in the field of view and basically meets the real-time performance.

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