An Intelligent Deep Learning and Computer Vision Framework for Automated Camouflaged Wildlife Animal Detection Using YOLOv11 and CAFEM-Lite
Keywords:
Boundary-aware attention, CAFEM-Lite, Camouflage detection, Camouflage Difficulty Index, COD10K, Conservation technology, DCT feature enhancementAbstract
The automated detection of wildlife fauna whose surface patterning and colouration have been refined by millions of years of evolutionary pressure to closely replicate the spectral and textural characteristics of their immediate habitat substrates represents one of the most formidable and consequential unsolved problems at the intersection of computer vision, deep learning, and ecological informatics. Extant general-purpose object detection architectures, engineered and benchmarked predominantly against scenes in which the target object is visually conspicuous against its background, sustain systematic and substantial performance degradation of 20 to 35 % points in mean average precision when confronted with camouflage scenarios, engendering directional bias in population estimates, IUCN status assessments, and anti-poaching surveillance efficacy. This paper presents CAFEM-Lite (Camouflage-Aware Feature Enhancement Module, lightweight variant), a modular, computationally parsimonious image-level preprocessing architecture that amplifies the subtle spectral and boundary signatures by which a camouflaged animal may be discriminated from its background before ingestion by the detection backbone. CAFEM-Lite concatenates three complementary sub-components in sequence—a DCT-based High-Frequency Amplifier (DCT-HFA) that selectively amplifies boundary-encoding high-frequency residuals in the frequency domain; a Boundary-Aware Convolutional Attention (BACA) mechanism that directs channel-level attention toward edge-rich feature regions through Sobel- and Laplacian-derived excitation weights; and a Progressive Context Fusion (PCF) gate that integrates local texture detail with broader semantic context through a learnable channel-partitioned mixing procedure. The entirety of CAFEM-Lite adds only 9,745 trainable parameters. Integrated with YOLOv11n and trained on the COD10K benchmark dataset over 30 epochs on an NVIDIA Tesla T4 GPU, the system achieves validation mAP@0.5 of 0.300, test-set Precision of 65.45%, and inference throughput of 47.3 FPS. A novel Camouflage Difficulty Index (CDI) provides an interpretable per-image quantification of detection hardness, and a browser-accessible Gradio application operationalises the complete pipeline on commodity hardware. The proposed framework constitutes a principled, deployable, and computationally accessible contribution to the nascent but consequential domain of AI-assisted wildlife conservation.
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