Abstract
Rapidly increasing concerns about the impact of tiny floating objects on water health has prompted the need for more effective detection methods. The main challenge in detecting these objects is their small size, accounting for only 0.5% of the image, which significantly hampers detection efforts. Moreover, existing object detectors utilize the intersection over union (IOU) as the bounding box regression loss to enhance object localization accuracy. However, this approach penalizes larger objects more heavily than smaller ones, leading to imbalanced regression losses. To address these issues, we propose enhancements to the YOLOv4 model. Our approach incorporates the following key improvements. Firstly, we introduce a feature augmentation module (FAM) to capture multi-scale contextual features of tiny objects and low-level features. This helps overcome the challenge of limited representation of tiny objects in the deeper layers of the network. Additionally, we integrate a convolutional block attention module (CBAM) into the path aggregation network to prevent the flooding of conflicting information in the fusion of features at different levels, ensuring an accurate representation of tiny object features. Finally, we propose a scale penalty function to address the issue of imbalanced regression loss. Experimental results demonstrate that our improved model achieves impressive detection performance on the Flow-RI dataset, specifically for detecting small-scale objects. These findings highlight the efficacy of our proposed methodology in enhancing the detection of tiny floating objects and contribute to the overall goal of improving water health.
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References
Wang M, Deng W (2021) Deep face recognition: a survey. Neurocomputing 429:215–244
Sundararaman R, De Almeida Braga C, Marchand E, Pettre J (2021) Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3865–3875
Prakash A, Chitta K, Geiger A (2021) Multi-modal fusion transformer for end-to-end autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7077–7087
Han J, Ding J, Xue N, Xia G-S (2021) Redet: a rotation-equivariant detector for aerial object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2786–2795
Medak D, Posilović L, Subašić M, Budimir M, Lončarić S (2022) DefectDet: a deep learning architecture for detection of defects with extreme aspect ratios in ultrasonic images. Neurocomputing 473:107–115
Wang K, Liu M, Ye Z (2021) An advanced YOLOv3 method for small-scale road object detection. Appl Soft Comput 112:107846
Bochkovskiy A, Wang C-Y, Liao H-YM (2020) YOLOv4: optimal speed and accuracy of object detection, arXiv preprintarXiv:2004.10934
Gai R, Chen N, Yuan H (2023) A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput Appl 35:13895–13906
Hu X, Liu Y, Zhao Z, Liu J, Yang X, Sun C, Chen S, Li B, Zhou C (2021) Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-v4 network. Comput Electron Agric 185:106135
Tzou T-L, Huang C-H, Lai Y-H, Tsai M-H, Hsu C-T, Chen P-S, Lee W-J (2022) Detect safety net on the construction site based on YOLO-v4. In: Innovative computing. Springer, pp 33–42
Chen Z-H, Juang J-C (2022) YOLOv4 object detection model for nondestructive radiographic testing in aviation maintenance tasks. AIAA J 60(1):526–531
Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19
Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759–8768
Yi Z, Yao D, Li G, Ai J, Xie W (2022) Detection and localization for lake floating objects based on CA-Faster R-CNN. Multimed Tools Appl 81(12):17263–17281
Li N, Huang H, Wang X, Yuan B, Liu Y, Xu S (2022) Detection of floating garbage on water surface based on PC-Net. Sustainability 14(18):11729
Lin F, Hou T, Jin Q, You A (2021) Improved YOLO based detection algorithm for floating debris in waterway. Entropy 23(9):1111
Renfei C, Jian W, Yong P, Zhongwen L, Hua S (2023) Detection and tracking of floating objects based on spatial–temporal information fusion. Expert Syst Appl 225:120185
Zhang L, Wei Y, Wang H, Shao Y, Shen J (2021) Real-time detection of river surface floating object based on improved RefineDet. IEEE Access 9:81 147-81 160
Yu X, Ye X, Zhang S (2022) Floating pollutant image target extraction algorithm based on immune extremum region. Digital Signal Process 123:103442
Cheng Y, Xu H, Liu Y (2021) Robust small object detection on the water surface through fusion of camera and millimeter wave radar. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 15263–15272
Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740–755
Chen C, Liu M-Y, Tuzel O, Xiao J (2016) R-CNN for small object detection. In: Asian conference on computer vision. Springer, pp 214–230
Kisantal M, Wojna Z, Murawski J, Naruniec J, Cho K (2019) Augmentation for small object detection, arXiv preprintarXiv:1902.07296
Yu X, Gong Y, Jiang N, Ye Q, Han Z (2020) Scale match for tiny person detection. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 1257–1265
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37
Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125
Leng J, Ren Y, Jiang W, Sun X, Wang Y (2021) Realize your surroundings: exploiting context information for small object detection. Neurocomputing 433:287–299
Bai Y, Zhang Y, Ding M, Ghanem B (2018) SOD-MTGAN: small object detection via multi-task generative adversarial network. In: Proceedings of the European conference on computer vision (ECCV), pp 206–221
Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6154–6162
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions, arXiv preprintarXiv:1511.07122
Jocher G (2022) ultralytics/yolov5: v6.1 [Online]. https://github.com/ultralytics/yolov5
Cheng G, Yuan X, Yao X, Yan K, Zeng Q, Han J (2022) Towards large-scale small object detection: survey and benchmarks, arXiv preprintarXiv:2207.14096
Lim J-S, Astrid M, Yoon H-J, Lee S-I (2021) Small object detection using context and attention. In: 2021 International conference on artificial intelligence in information and communication (ICAIIC). IEEE, pp 181–186
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Sun D, Yang Y, Li M, Yang J, Meng B, Bai R, Li L, Ren J (2020) A scale balanced loss for bounding box regression. IEEE Access 8:108438–108448
Cheng Y, Zhu J, Jiang M, Fu J, Pang C, Wang P, Sankaran K, Onabola O, Liu Y, Liu D et al (2021) Flow: a dataset and benchmark for floating waste detection in inland waters. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10953–10962
Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with warm restarts. arXiv preprintarXiv:1608.03983
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv preprintarXiv:1412.6980
Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988
Faster R (2015) Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 9199(10.5555):2 969 239-2 969 250
Ge Z, Liu S, Wang F, Li Z, Sun J (2021) YOLOX: exceeding yolo series in 2021, arXiv preprintarXiv:2107.08430
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Li, K., Wang, Y., Li, W. et al. Feature augmentation and scale penalty for tiny floating detection. Int. J. Mach. Learn. & Cyber. 15, 853–862 (2024). https://doi.org/10.1007/s13042-023-01943-1
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DOI: https://doi.org/10.1007/s13042-023-01943-1