Citation: | WANG Chi, SHEN Chen, HUANG Qing, ZHANG Guo-feng, LU Han, CHEN Jin-bo. Self-supervised learning enhancement and detection methods for nocturnal animal images[J]. Chinese Optics. doi: 10.37188/CO.2024-0011 |
In order to solve the problems of low image exposure, low contrast and difficulty of feature extraction in real-time animal monitoring at night, we proposed a lightweight self-supervised deep neural network Zero-Denoise and an improved YOLOv8 model for image enhancement and accurate recognition of nocturnal animal targets. The first stage of rapid enhancement was performed by lightweight PDCE-Net. A new lighting loss function was proposed, and the second stage of re-enhancement was carried out in PRED-Net based on the Retinex principle and the maximum entropy theory, using the original image and fast enhancement image corrected by the parameter adjustable Gamma. Then, the YOLOv8 model was improved to recognize the re-enhanced image. Finally, experimental analysis was conducted on the LOL dataset and the self-built animal dataset to verify the improvement of the Zero-Denoise network and YOLOv8 model for nocturnal animal target monitoring. The experimental results show that the PSNR, SSIM, and MAE indicators of the Zero-Denoise network on the LOL dataset reached 28.53, 0.76, and 26.15, respectively. Combined with the improved YOLOv8, the mAP value of the baseline model on the self-built animal dataset increased by 7.1% compared to YOLOv8. Zero-Denoise and improved YOLOv8 can achieve good quality images of nocturnal animal targets, which can be helpful in further study of accurate methods of monitoring these targets.
[1] |
QI Y L, YANG ZH, SUN W H, et al. A comprehensive overview of image enhancement techniques[J]. Archives of Computational Methods in Engineering, 2022, 29(1): 583-607. doi: 10.1007/s11831-021-09587-6
|
[2] |
刘彦磊, 李孟喆, 王宣宣. 轻量型YOLOv5s车载红外图像目标检测[J]. 中国光学(中英文),2023,16(5):1045-1055. doi: 10.37188/CO.2022-0254
LIU Y L, LI M ZH, WANG X X. Lightweight YOLOv5s vehicle infrared image target detection[J]. Chinese Optics, 2023, 16(5): 1045-1055. (in Chinese). doi: 10.37188/CO.2022-0254
|
[3] |
HU H F. Illumination invariant face recognition based on dual-tree complex wavelet transform[J]. IET Computer Vision, 2015, 9(2): 163-173. doi: 10.1049/iet-cvi.2013.0342
|
[4] |
MUNIAN Y, MARTINEZ-MOLINA A, MISERLIS D, et al. Intelligent system utilizing HOG and CNN for thermal image-based detection of wild animals in nocturnal periods for vehicle safety[J]. Applied Artificial Intelligence, 2022, 36(1): 2031825. doi: 10.1080/08839514.2022.2031825
|
[5] |
MURUGAN R A, SATHYABAMA B. Object detection for night surveillance using Ssan dataset based modified Yolo algorithm in wireless communication[J]. Wireless Personal Communications, 2023, 128(3): 1813-1826. doi: 10.1007/s11277-022-10020-9
|
[6] |
BHATT D, PATEL C, TALSANIA H, et al. CNN variants for computer vision: history, architecture, application, challenges and future scope[J]. Electronics, 2021, 10(20): 2470. doi: 10.3390/electronics10202470
|
[7] |
任凤雷, 周海波, 杨璐, 等. 基于双注意力机制的车道线检测[J]. 中国光学(中英文),2023,16(3):645-653. doi: 10.37188/CO.2022-0033
REN F L, ZHOU H B, YANG L, et al. Lane detection based on dual attention mechanism[J]. Chinese Optics, 2023, 16(3): 645-653. (in Chinese). doi: 10.37188/CO.2022-0033
|
[8] |
LI CH Y, GUO J CH, PORIKLI F, et al. LightenNet: a Convolutional Neural Network for weakly illuminated image enhancement[J]. Pattern Recognition Letters, 2018, 104: 15-22. doi: 10.1016/j.patrec.2018.01.010
|
[9] |
DING X, HU R M. Learning to see faces in the dark[C]. 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2020: 1-6.
|
[10] |
LI CH Y, GUO CH L, CHEN CH L. Learning to enhance low-light image via zero-reference deep curve estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 40(8): 4225-4238.
|
[11] |
JIANG Y F, GONG X Y, LIU D, et al. EnlightenGAN: deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 2021, 30: 2340-2349. doi: 10.1109/TIP.2021.3051462
|
[12] |
FU Y, HONG Y, CHEN L W, et al. LE-GAN: Unsupervised low-light image enhancement network using attention module and identity invariant loss[J]. Knowledge-Based Systems, 2022, 240: 108010. doi: 10.1016/j.knosys.2021.108010
|
[13] |
WANG R J, JIANG B, YANG CH, et al. MAGAN: Unsupervised low-light image enhancement guided by mixed-attention[J]. Big Data Mining and Analytics, 2022, 5(2): 110-119. doi: 10.26599/BDMA.2021.9020020
|
[14] |
MONAKHOVA K, RICHTER S R, WALLER L, et al. Dancing under the stars: video denoising in starlight[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2022: 16220-16230.
|
[15] |
CHEN J R, KAO S H, HE H, et al. Run, Don't walk: chasing higher FLOPS for faster neural networks[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2023: 12021-12031.
|
[16] |
WEI CH, WANG W J, YANG W H, et al. Deep retinex decomposition for low-light enhancement[C]. British Machine Vision Conference 2018, BMVA Press, 2018: 155.
|
[17] |
ZHANG Y, DI X G, ZHANG B, et al. Self-supervised low light image enhancement and denoising[J]. arXiv preprint arXiv: 2103.00832, 2021.
|
[18] |
ZHANG Q L, YANG Y B. SA-Net: Shuffle attention for deep convolutional neural networks[C]. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2021: 2235-2239.
|
[19] |
XU X ZH, JIANG Y Q, CHEN W H, et al. DAMO-YOLO: a report on real-time object detection design[J]. arXiv preprint arXiv: 2211.15444, 2022.
|
[20] |
TONG Z J, CHEN Y H, XU Z W, et al. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism[J]. arXiv preprint arXiv: 2301.10051, 2023.
|
[21] |
WU W H, WENG J, ZHANG P P, et al. URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2022: 5891-5900.
|
[22] |
ZHANG F, LI Y, YOU SH D, et al. Learning temporal consistency for low light video enhancement from single images[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2021: 4965-4974.
|
[23] |
MA L, MA T Y, LIU R SH, et al. Toward fast, flexible, and robust low-light image enhancement[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2022: 5627-5636.
|
[24] |
LV F F, LU F, WU J H, et al. MBLLEN: Low-light image/video enhancement using CNNs[C]. British Machine Vision Conference 2018, BMVA Press, 2018: 220.
|