In strong light environments,images often appear overexposed,which seriously impacts the accuracy of target detection.Most existing research,however,requires additional modules to assist in detection,which affects the...In strong light environments,images often appear overexposed,which seriously impacts the accuracy of target detection.Most existing research,however,requires additional modules to assist in detection,which affects the timeliness of the detection process.To address the issues of reduced target detection accuracy and timeliness in overexposed environments,this paper proposes a realtime anti-light target detection improvement algorithm based on you-only-look-once v8n(YOLO v8n),focusing on enhancing the model’s ability to extract features from overexposed images without the need for additional modules.Firstly,online overexposure enhancement technology is integrated into model training to simulate overexposed images produced in overexposed environments,enhancing the model’s robustness in detecting overexposed environments.Deformable convolution networks v2 is used to improve the cross-stage partial bottleneck with two convolutions layer,addressing the issue of traditional convolution’s poor feature extraction performance for overexposed images,thereby aiding the model in capturing targets with weakened or missing features and enhancing the model’s ability to construct the geometric shape of targets.Secondly,large separable kernel attention is introduced to enhance the spatial pyramid pooling fast layer,strengthening the model’s overall connectivity for targets with missing features.Finally,distance intersection over union is utilized to optimize the detection accuracy of overlapping targets in overexposed environments.The experimental results show that,compared to the original model,the mAP50 and mAP50–95 of the model designed in this paper are improved by 23.2%and 15.7%,respectively,and the model size only increases by 0.3 M.While improving detection accuracy,the lightweight requirements for actual deployment are also met.展开更多
基金supported by the Natural Science Foundation of Guangdong Province(Grant No.2022A1515010011)the Basic and Theoretical Science and Technology Programme of Jiangmen City(Grant No.2023JC01020)in 2023。
文摘In strong light environments,images often appear overexposed,which seriously impacts the accuracy of target detection.Most existing research,however,requires additional modules to assist in detection,which affects the timeliness of the detection process.To address the issues of reduced target detection accuracy and timeliness in overexposed environments,this paper proposes a realtime anti-light target detection improvement algorithm based on you-only-look-once v8n(YOLO v8n),focusing on enhancing the model’s ability to extract features from overexposed images without the need for additional modules.Firstly,online overexposure enhancement technology is integrated into model training to simulate overexposed images produced in overexposed environments,enhancing the model’s robustness in detecting overexposed environments.Deformable convolution networks v2 is used to improve the cross-stage partial bottleneck with two convolutions layer,addressing the issue of traditional convolution’s poor feature extraction performance for overexposed images,thereby aiding the model in capturing targets with weakened or missing features and enhancing the model’s ability to construct the geometric shape of targets.Secondly,large separable kernel attention is introduced to enhance the spatial pyramid pooling fast layer,strengthening the model’s overall connectivity for targets with missing features.Finally,distance intersection over union is utilized to optimize the detection accuracy of overlapping targets in overexposed environments.The experimental results show that,compared to the original model,the mAP50 and mAP50–95 of the model designed in this paper are improved by 23.2%and 15.7%,respectively,and the model size only increases by 0.3 M.While improving detection accuracy,the lightweight requirements for actual deployment are also met.
