The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications. However, the performance of traditional object detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional encoder–decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the loss of information and the interference of noises in the process of feature interweaving. Furthermore, we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that might become the edges of an object. Experimental results on an edge device show that CODCEF achieved competitive results compared with 10 state-of-the-art methods.

Nonparametric statistics methods are a class of robust and potent machine learning operators, which are widely used in various domains such as finance, medicine, and computer science. Such methods deliver an accurate estimation without an assumed data distribution. Moreover, they can handle discrete data with various data sources. Despite their desirable features, the calculation of large-scale nonparametric statistics is both compute- and memory-intensive, and the performance overhead hinders them from widespread usage. This paper identifies that the key performance bottleneck lies in the rank-based operations which are intensively involved in variants of nonparametric statistics methods. These rank-based operations can thereby be fully accelerated and structurally reused among diverse statistics. We then introduce Hynify, a high-throughput and unified accelerator that facilitates a rich set of nonparametric statistics. To ensure comprehensiveness, we capture three primary computational paradigms of nonparametric statistical methods, namely, aggregation, pair-wise rank, and concordance, with the right architecture designs. To improve throughput, Hynify exploits fine-grained computation and pipelining for increased performance. We implement Hynify in FPGA demonstration and representative experimental results demonstrate that Hynify delivers up to 160x/21x throughput improvement over GPU and 64-core CPU, respectively, while achieving up to 781×/62× energy efficiency improvement.