Light-resistant target detection improvement algorithm for overexposed environments
Bibliographic record
Abstract
Abstract 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 real-time 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".